Medical failure pattern search engine

ABSTRACT

A patient safety search engine and alarm processor is programmed to repetitively search the electronic medical records of all patients in a hospital system to automatically provide early detection of patients with evolving pathophysiologic cascades, and in particular the cascades of evolving death, such as cascades of septic shock. The search engine also searches for a wide range of evolving pathophysiologic failures which are commonly fatal if detected too late. An alarm processor is provided which is programmed to provide an alarm upon the detection of a cascade or failure. The processor is further be programmed to provide an image of the cascade, and to determine, the severity of the cascade, and the time of onset of the cascade in relation to the timing and type of procedures and treatment and the increased cost associated with the cascade. Discretionary real-time system-wide searches for a wide range of clinical failure patterns or images within the hospital system may be performed using the disclosed patient safety search engine.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.61/126,906, filed May 8, 2008 and to U.S. Provisional Application No.61/200,162, filed Nov. 25, 2008, the disclosures of which are herebyincorporated by reference in their entirety for all purposes. Thisapplication is related to co-filed US patent application titled “PatientSafety Processor” the disclosure of which is hereby incorporated byreference in its entirety for all purposes.

BACKGROUND

The present disclosure relates systems and methods for detecting andmonitoring patient conditions in clinical medicine settings.

Patients die unexpectedly on hospital wards under the careful watch ofeven knowledgeable and diligent healthcare workers at alarming rates. Ithas been argued that hospitals have a culture of failure tolerance.However, a more critical analysis reveals that this “tolerance” isactually resignation and that the high number of clinical failurescomprises the unavoidable result of the ill-conceived attempt to managethe profound complexity of overlapping human pathophysiology withoutadequate technology. Unfortunately hundreds of common but subtleperturbations which combine to produce complex pathophysiologic failurecascades which progress to death can potentially occur with everypatient in the hospital.

While the physiologic complexity of just one patient is oftenoverwhelming, a single nurse may have twelve complex patients and asingle hospitalist physician may have 30. In the present state ofhospitals, most of the physiologic complexity resides in the electronicmedical records (EMR) even as the patient progresses toward death.Unless an expert physician or nurse puts all the pieces together timelyto see the evolving failure, the patient is often doomed even thoughhealthcare workers are nearby.

Patient care in a hospital setting involves a complex management processbecause human pathophysiology is highly complex and healthcare workersaddress multiple patient issues simultaneously. Decisions about patientpriority and care made by the healthcare workers are subjective to somedegree and may vary depending on the level of expertise and experienceof each person involved in patient care.

Because of the complexity involved in patient care, particularly in ahospital setting, healthcare workers have attempted to provide a levelof uniformity to the process through protocol-based care. Such care mayinvolve “if X-threshold-breach then Y-action” branching decision treeprotocols. However, such protocols when considered in relation to thetrue level of pathophysiologic complexity often comprise a profound oversimplification so that the healthcare worker can easily proceed down thewrong branch of a decision tree.

In addition to protocol-based care, healthcare workers often monitorvarious physiological parameters of a patient in order to obtain moreinformation upon which they may base clinical care decisions. Many ofthese parameters may include blood oxygen levels, pulse rate, routineblood tests and vital sign tests, which may be recorded in a centralizedelectronic medical record. However, this testing may not be effective inthe early detection of certain clinical conditions or in providing thehealthcare worker with a clear picture of the patient's condition andcare. Even subtle and minor levels of perturbation may lead to profoundinstability in certain clinical situations. For example, minor changesin the serum sodium in the setting of a stroke may lead to confusion andthen obtundation, which may increase the risk of aspiration, pneumonia,and venous thrombosis. Indeed, the level of serum sodium decrement toproduce such abnormalities may be as little as 8 mEq, a decrement whichwould otherwise not be likely to produce an adverse reaction in theabsence of an acute stroke. Since an 8 mEq decline in serum sodium wouldnormally be tolerated in the absence of a stroke, it may be easilyoverlooked as a cause of profound instability by a healthcare worker whomay not be knowledgeable or diligent enough to recognize the entirerelational complexity. It is very common that subtle or simple events oroccurrences actually comprise linked components of a much larger,dangerous, but undetected expanding pathophysiologic failure process.Since simple pertubations are readily overlooked by the physician (or ifthey are identified, the pivotal linkage to other processes is commonlyunrecognized), this allows the pathophysiologic failure process toprogress, untreated toward death.

In another example of the challenges involved in the timely detection ofevolving complex patient conditions, septic shock is often the endresult of progression from the uncomplicated state of infection toprogressive states of the inflammatory response syndrome, sepsis, severesepsis, and finally septic shock. These distinctions of states arearbitrary and poorly defined at the bedside. The vast majority ofpatients have infection with fever without further progression and manyeven progresses to the inflammatory response syndrome without furtherprogression to septic shock. Because routine blood testing and evencontinuous vital measurements may not always detect the pre-shock state,specialized blood tests and biomarker profiles specifically developed todetect the pre-septic shock state have been developed. However, specificblood test and profiles suffer from a lack of specificity, in partbecause the variable response of patients to physiologic perturbation.Whether or not a given patient progresses to shock depends on much morethan the biomarkers present and their concentrations. Progression toshock may depend on a complex relationship of patient-specificphysiologic responses to immunologic and inflammatory perturbation aswell as the physiologic state of the patient at the onset and during theperturbation and the timeliness and adequacy of intervention (e.g.antibiotics and/or fluid). Since most of these factors are not capturedby blood test measurements or biomarker profiles, even serial testingdirected specifically toward the detection of the pre-shock state maynot provide sufficient information to provide for reliable timelydetection of the evolving state of severe sepsis or septic shock.

BRIEF DESCRIPTION OF THE DRAWINGS

Advantages of the present disclosure may become apparent upon readingthe following detailed description and upon reference to the drawings inwhich:

FIG. 1 is an exemplary component diagram of a patient demonstrating theoverlapping patient complexities that may be used to constructrelational binaries, image components and MPPC for searching anddetection;

FIG. 2 is a diagram depicting the levels of analysis in accordance withan exemplary embodiment;

FIG. 3A is a data flow diagram in accordance with an exemplaryembodiment;

FIG. 3B is a diagram of an exemplary system in accordance with anexemplary embodiment;

FIG. 3C is a data and action flow diagram in accordance with anexemplary embodiment;

FIG. 4 is an exemplary UML Static Diagram of the primary classes withinone embodiment of a relational binary processor;

FIG. 5 is an exemplary UML Static Diagram of a subset of the relationalbinary processor specifically expanding the definition of the eventtype;

FIG. 6 is an exemplary UML Static Diagram of the primary classes withinthe patient safety processor;

FIG. 7 is an exemplary UML Static Diagram of the primary classes withinthe binary definition set;

FIG. 8 is an exemplary UML Static Diagram of the primary classes withinthe Failure image component Definition Set;

FIG. 9 is an exemplary user interface model of the convergence editorthat depicts a sleep apnea binary diagram;

FIG. 10 is an exemplary user interface model of the aggregate failureimage component editor that depicts a failure image component diagramassociated with narcotic-induced ventilation instability;

FIG. 11 is an exemplary user interface model of the convergence editorthat depicts a heparin therapy binary diagram;

FIG. 12 is an exemplary user interface model of the convergence editorthat depicts an insulin therapy binary diagram;

FIG. 13 is an exemplary user interface model of the convergence editorthat depicts a narcotic therapy binary diagram;

FIG. 14 is an exemplary user interface model of the aggregate failureimage component editor that depicts a failure image component diagramassociated with heparin-induced hemorrhage;

FIG. 15A is a failure image frame that includes a plurality of timelinesorganized into groupings showing an expanding cascade of evolving deathdue to septic shock.

FIG. 15B is a failure image frame that includes a plurality of timelinesorganized into groupings showing a failure image of an expanding cascadeof septic shock with portions of the image being separated intosequential states.

FIG. 15C is a failure image frame that includes a plurality of timelinesorganized into groupings showing early timepoints in an expandingcascade of severe septic shock;

FIG. 15D is a failure image frame which shows an image of a failurecascade severe septic shock with inflammatory, hemodynamic, andrespiratory augmentation, and with early immune failure;

FIG. 15E is a failure image frame which shows an image of a failurecascade of severe septic shock with inflammatory, hemodynamic, andrespiratory augmentation, with immune failure, and with evidence ofdecline in respiratory gas exchange and fall in platelet count;

FIG. 15F is an exemplary failure image frame showing an image of anadvanced cascade of severe septic shock with progression to metabolicfailure, renal failure, hemodynamic failure and respiratory failure;

FIG. 16 is an exemplary congestive heart failure (CHF) failure imagethat includes a plurality of timelines organized into groupings;

FIG. 17 is an exemplary sleep apnea failure image that includes aplurality of timelines organized into groupings;

FIG. 18 is an exemplary thrombocytopenic purpura failure image thatincludes a plurality of timelines organized into groupings;

FIG. 19 shows an overview image of perturbation onset and progressionfrom the time lapsed MPPC of FIG. 15A, wherein the perturbations in eachgrouping are incorporated into an aggregate index along a singlesmoothed time series for each group;

FIG. 20 is a general failure image that includes a plurality oftimelines from the complexity diagram of FIG. 1 showing a failure imageof excessive secretion of serum inappropriate antidiuretic hormone(SIADH) induced fall in hyponatremia;

FIG. 21 is a split screen diagram of a drag and drop interface forconstructing combined physiologic and treatment images for the patientsafety processor showing the construction of an MPPC indicative ofnarcotic-associated recovery failure in the presence of sleep apnea;

FIG. 22 is an exemplary image frame of a failure image editor forconstructing a septic shock MPPC for recognition by the Patient SafetyProcessor; and

FIG. 23 is a diagram of an exemplary patient safety processor network.

DETAILED DESCRIPTION

One or more specific embodiments of the present disclosure will bedescribed below. In an effort to provide a concise description of theseembodiments, not all features of an actual implementation are describedin the specification. It should be appreciated that in the developmentof any such actual implementation, as in any engineering or designproject, numerous implementation-specific decisions must be made toachieve the developers' specific goals, such as compliance withsystem-related and business-related constraints, which may vary from oneimplementation to another. Moreover, it should be appreciated that sucha development effort might be complex and time consuming, but wouldnevertheless be a routine undertaking of design, fabrication, andmanufacture for those of ordinary skill having the benefit of thisdisclosure.

The present disclosure provides systems and methods for diagnosis,monitoring, and treatment of certain clinical conditions.

One embodiment comprises a processor system including an electronicmedical records database of a hospital or hospital system containing atleast laboratory and physiologic data of at least one patient, a searchengine programmed to automatically and repetitively search data withinor derived from the database to detect complex patterns or images ofevolving pathophysiologic cascades, and to further define the cascade,quantify the cascade, and to determine the relationships and cost of thecascade. According to one aspect of the invention a pathophysiologiccascade as detected by one embodiment comprises an expandingpathophysiologic process. Such expansion commonly occurs within theinitially affected system as for example in the immune system (as aninflammation cascade) and then expands into other systems such as therespiratory and cardiovascular system often through chemical,neurological, and/or anatomical mechanisms of augmentation, upregulation, down regulation, compensation, compensation failure, andcombined systems failure. The most important cascades detected by anembodiment of the present invention are cascades of evolving death(CED).

One embodiment comprises a search engine which automatically,intermittently and/or continuously searches for and detectspathophysiologic cascades and particularly cascades of evolving death(CED), and an alarm processor programmed to identify the patient whichis generating the CED and to provide an alarm upon the detection of sucha cascade at a site adjacent the location of the patient, to a caregiver managing the patient, to a ward in which the patient resides, to aquality control center or patient safety management center, to thepatient him or herself as by a pager or phone which may be configured todisplay an image of the cascade, the type of the cascade, and/or atleast one characteristic of the cascade. The pager may generate a seriesof lights which are indicative of the severity of the failure and/orcascade detected. The wearing of the pager by the patient prevents thehealthcare worker from discounting or ignoring the findings since thepatient him or herself (or the patients family if the patient is notcompetent) is also notified by the processor.

According to one aspect of the present invention, cascades of evolvingdeath (CED) are detected by the search engine as expanding aggregationsof perturbations and variations of signals and/or tests derived from abiologic organism which spreads across signal and tests derived fromdifferent systems within the organism and commonly ends in death.Commonly, as the CED evolves, the number of perturbations, the number ofdifferent types of positive or negative trends or variations, and/or thenumber of different types of threshold breaches, and the number ofperturbed systems, progressively rise.

One pathophysiologic process which commonly generates a widely expandedCED is severe sepsis. Severe sepsis commonly induces microcirculatoryfailure which eventually expands the CED across all systems dependent onmicrocirculation. In the lungs, evolving microcirculatory failure causesa progressive decline in the efficiency of gas exchange, minuteventilation rises to compensate or as a direct result of the factors(such as toxins) which are associated with the cause of the process.With many systems the response is biphasic, with an initial augmentationperturbation (with comprises a favorable or preparatory response of thesystem. As the cascade progresses, subsequent to augmentation, failurerelated perturbations develop expansively. In the sepsis CED example,metabolic system perturbation may initially be an augmentationperturbation comprising a simple preparatory fall in hydrogen ion, butlater as the cascade evolves widespread failure related perturbationsdominate.

Cascade of evolving death commonly contain smaller relational patternswhich may progress virtually throughout the cascade and by themselvesmay portend death. For example the CED of severe sepsis contains thepattern of pathophysiologic divergence of ventilation and arterialoxygen saturation which is described in U.S. patent Ser. No. 10/150842(the disclosure of which is incorporated by reference as if completelydisclosed herein). Despite the fact that such smaller relationalpatterns may progress virtually throughout the CED (and by themselvesmay portend death) these small patterns generally represent only a verysmall portion of the signal “bandwidth” of the death cascade, especiallyas the cascade matures and becomes widely expanded. The smallerrelational patterns therefore are useful for detection of the likelypresence of a CED but do not provide the specificity to determine thecause of the CED and may not be, especially early in theirmanifestation, specific for the presence of a CED.

Typical CED have at least one initiating apex or vertex which comprisesthe onset of the cascade. The apex or vertex is generally within asingle physiologic system. The CED expands out from the apex or vertexacross the initially affected system and/or into and across othersystems. It is common for CED to expand within the initial system first.Like a progressively enlarging cone of perturbation projecting initiallywithin and then beyond the initial system, the CED may expand to involvevirtually all systems by the time the point of death is reached.

Cascades of evolving death, in 3D space can be represented by a conewith apex comprising the onset of the cascade, the length of the conebeing defined by cascade duration, the angle of the come being definedby cascade expansion rate, and the cross-sectional area at a given pointbeing defined by the magnitude of the cascade expansion at that point intime.

When, according to one embodiment of the present invention, the datasets are organized into a 2D format (such as a time series matrix whichis compartmentalized such that each system defines a separatecompartment of time series) the CED commonly produces a triangle withthe angle at the vertex defining, in part, the speed of expansion of theCED. The CED, without intervention, will commonly end in death. In the2D representation a death vertex is identified on the X axis and thetriangle can be competed by passing a line vertically through allperturtubations or variations which remain at the time of death. Thisforms the base of the triangle of the CED.

If the patient achieves spontaneous or assisted recovery the cascadebegins to contract. The point at which contraction begins forms thevertex and base of the cascade expansion triangle of the CED and thevertex and base of contraction triangle of the CED which eventuallycontracts to a stable state vertex point at the end of the contractiontriangle. Even after a patient has recovered, the state of thephysiologic components of the time series matrix of the stable stateafter the CED may be different from that which preceded the CED and thisdifference and its location in relation to systems and time series typesis often a quantifiable indication of the extent and type of residualinjury sustained by the patient due to the event which induced the CED,the CED itself, and/or due to treatment.

While the term cone and triangle is used herein for graphicalrepresentation of the expanding cascade, a preformatted matrix will beaffected in a wide range of expanding patterns. Expansion may not beuniform or linear but rather the shape of the expanding cascade variesdepending on a wide range of factors as will be discussed.

The processor may further be programmed to determine at least one of thetype of cascade, the severity of the cascade, the duration of thecascade, the time of onset of the cascade, the maturity (as for examplethe stage) of the cascade. The severity may be determined by the numberof perturbations or trends which comprise the cascade, the severity ofthe pertubations or trends (as for example by the slope and/or magnitudeof the perturbations or trends which comprise the cascade in relation tothe baseline values and/or statistical normal values by another severitymeasure), the number of systems affected by the cascade, the presence,number and /or severity of failure of compensation in response toperturbations associated with the cascade, the growth of the cascade, asfor example by the number of new perturbations being added per unit timeor the number of systems affected. The processor may also detect theevents or components associated with or which are a part of the cascade.The alarm processor may be programmed to provide an indication of eachof the forgoing. Alarm display may be provided for presenting any of theforgoing in textual, auditory, graphical, or other formats. The searchmay be reinitiated each time new data is added, each time a particulartype of data is added, or at a preselected or adaptive frequency. Forexample the searching frequency may increase when early components of apossible cascade have been identified or when an actual cascade has beenidentified.

One embodiment retains the images and relationships detected during theprevious search(s) so that the subsequent new search cycle of the dataset is less processor intense involving, for example, only thecomparison of the new data (with or without prior formatting) to theprevious processed data sets to determine if a new cascade isdeveloping, an existing cascade is becoming more severe or improving, orif another event in relation to a cascade (such as a treatment event)has occurred.

One embodiment comprises a patient data processing system for convertingthe global electronic medial record (EMR) of a patient or patients intoa real-time patient monitor comprising a pathophysiologic cascade searchengine configured to repetitively and/or continuously search the EMR forevolving complex pathophysiologic cascades and an alarm processor foroutputting a warning upon detection of a cascade. The pathophysiologicfailure cascade search engine may be programmed to continuously searchthe EMR for evolving complex images of physiologic failure such, forexample a sepsis cascade, the alarm processor may be programmed toprovide an alarm upon detection of the pathophysiologic failure cascade,and the image processor programmed to output an image of the evolvingfailure cascade. The data processing system may also be programmed toquantify the cascade, track the progression of the cascade, identify,highlight and/or alarm associated events in relation to the cascade,determine the cost associated with the cascade, determine the timing oftreatment in relation to the cascade, and determine the response of thecascade in relation to treatment.

In another embodiment the processor is programmed to convert theelectronic medical records into a particular format favorable forsearching for pathophysiologic cascades. In one example such a formatcomprises sequential and timed trends comprised of at least positivetrends and negative trends of both the physiologic parameters and thelaboratory data, detect relational trends comprised of a combination ofpositive and/or negative trends, detect complex cascade patternscomprised of a plurality of combinations of relational trends,automatically output a display of the image of the detected complexcascade, automatically output a warning indicating the detection of thecomplex cascade, track the growth or decline of the complex cascade andoutput an indication indicative of growth or decline the cascade patternmay be indicative of a single or multiple physiologic failures such asat least one of sepsis, severe sepsis, septic shock, andmicrocirculatory failure, a shock cascade, and a septic shock cascade toname a few. The processor may be programmed to determine and output anindication of the type of the cascade detected, to determine and outputat least an indication of the timing and type of the trends along thecascade, to determine and output at least an indication of the length ofthe cascade, to detect the onset of therapy and to determine and outputat least an indication of timing of therapy in relation to the cascade.The patient data processing system may comprise a computer be programmedto search the EMR to detect sequential and timed trends comprised of atleast positive and negative trends of both the physiologic parametersand the laboratory data, determine relational timing of the detectedpositive and negative trends, detect complex cascade patterns comprisedof a plurality of combinations of positive and negative trends evolvingin sequential timed relation to each other, and output an indication ofthe detected complex relational cascade pattern.

In another embodiment, the processing system may comprising a computerprogrammed to convert the electronic medical records into sequential andtimed trends comprised of at least positive trends and negative trendsof both the physiologic parameters and the laboratory data, detectrelational trends comprised of a combination of positive and/or negativetrends, detect complex cascade patterns comprised of a plurality ofcombinations of relational trends, output an alarm indicating thedetection of the complex cascade pattern.

Another embodiment comprises a patient data processing system forprocessing electronic medical records of at least physiologic parametersand laboratory data of at least one patient comprising a computerprogrammed to identify positive and negative trends comprising at leasta combination of inflammatory trends, metabolic trends, hemodynamictrends, hematologic trends, and respiratory trends, identify therelational timing of the positive and negative which relationally orcollectively are indicative of the septic shock or pre-septic shockfailure cascade, and identify and output an indication of the septicshock or pre-septic shock failure cascade. The may be further programmedto identify the onset of treatment, and identify the timing of treatmentin relation to at least one component of the cascade and to analyze therelational pattern to identify the earliest trend comprising a componentof the cascade, identify the onset of treatment, and identify the timingof treatment in relation to said earliest trend. In another embodimentfor processing electronic medical records of at least physiologicparameters and laboratory data comprising a computer programmed togenerate a large set of time-series of data of a patient including atleast data relating to the physiologic state and/or care of a patient,convert the datasets, including at least the monitored datasets andlaboratory datasets into parallel and overlapping time series, identifyoccurrences comprising at least, inflammatory occurrences, metabolicoccurrences, volumetric occurrences, hemodynamic occurrences, therapyoccurrences, hematologic occurrences, respiratory occurrences, identifythe timing of the occurrences, identify at least one relational patternof occurrences along a plurality of time series which is indicative offailure cascade of at least one of a sepsis cascade, a pulmonaryembolism cascade, a metabolic cascade, and a microcirculatory failurecascade, output at least one of an indication of the cascade, the timingand type of the occurrences along the cascade, and length of thecascade. Another embodiment comprises a method for converting the globalelectronic medial record into a patient monitors the method theelectronic medical record system having a display comprising steps ofconverting the electronic medical records into sequential and timedtrends comprised of at least positive trends and negative trends of boththe physiologic parameters and the laboratory data, detecting relationaltrends comprised of a combination of positive and/or negative trends,detecting complex cascade patterns comprised of a plurality ofcombinations of relational trends, outputting a display of the image ofthe detected complex cascade, outputting a warning indicating thedetection of the complex cascade pattern, and tracking the growth ordecline of the complex cascade and output an indication indicative ofgrowth or decline.

In another embodiment the patient data processing system for processingelectronic medical records of at least one patient comprising a computerprogrammed to convert at least the physiologic and laboratory data ofthe electronic medical records into a predetermined format for imaging,imaging the formatted electronic medical record, detect an imageindicative of at least one of patient physiology and patient care,output an indication of the presence of physiologic failure. Thecomputer may be further programmed to analyze the images to detectrelational patterns of the detected physiologic failure to determine theseverity of the physiologic failure and/or to detect relational patternsindicative of the patient response to the detected physiologic failureto determine the severity of the physiologic failure and/or to detectrelational patterns indicative of patient care in response to thedetected physiologic failure to determine at least one of the timelinessand efficacy of the care. The physiologic failure can for example be atleast one of sepsis, severe sepsis, septic shock, a sepsis cascade,microcirculatory failure, a shock cascade, a septic shock cascade toname a few.

The predefined format may comprise a time series matrix, an objectifiedtime series matrix, or anther format. The predetermined format mayinclude at least one region comprised of at least one collection of timeseries of specific physiologic components. For example at least one ofinflammation indicators, respiratory indicators, cardiovascularindicators, and metabolic indicators to name a few. The predefinedformat can comprise a plurality of regions comprised of a plurality ofcollections of time series of different specific physiologic components.The images may be comprised of aggregations of variations of physiologicdata and laboratory data, the variations having positive or negativeslopes, and/or aggregations of relational variations of positive and/ornegative trends of physiologic data combined with positive and/ornegative trends of laboratory data.

The patient data processing system may include an image archive systemfor archiving images of physiologic failure and for sharing these withother processors to grow the general archive and knowledge of thedifferent images and variations of the images of failure. The patientdata processing system may convert the time series matrix into apredetermined format of the time series matrix and image the formattedtime series matrix to detect an image indicative of physiologic failure.

One embodiment comprises a patient data processing system having anobject recognition system, for processing medical records of at leastone patient comprising a computer programmed to convert the medicalrecords of at least the physiologic and laboratory data of at least onepatient into a time series matrix defining vertical and horizontal axes,and, using the object recognition system, search the time series matrixcontinuously or intermittently for a cascading plurality of relationalpatterns indicative of evolving physiologic failure along both thevertical and horizontal axes of the matrix.

Another embodiment comprises a patient data processing system foranalyzing electronic medical records for real-time detection ofphysiologic failure comprising steps of continuously or intermittentlysearch the medical records to detect events along the time seriesmatrix, detect relational events along the time series matrix comprisedof the detected events, detect relational cascade patterns comprised ofa plurality of combinations of relational events, take action based onthe detection of the at least one pattern wherein, for example, thepattern is indicative of physiologic failure.

In one embodiment the search engine is programmed to detect cascadescomprised of at least a plurality of linked perturbations and trends ofphysiologic and laboratory data associated with relational compensationcreating a progressively enlarging aggregation of progressively greaternumbers of perturbed physiologic and laboratory data. The processor maybe further programmed to determine at least one characteristic of thecascade, the characteristic comprising at least one of, the severity ofthe cascade, the duration of the cascade, the time of onset of thecascade, and the maturity of the cascade, the timing relationship of thecascade to other events or other cascades, the cost associated with thecascade, the global pattern of the cascade, the time of termination ofthe cascade, the components of the cascade, the state of evolution ofthe cascade, the length of stay subsequent to or in association with thecascade, the treatments associated with the cascade. The characteristicof the cascade may be defined by, for example, the number ofperturbations and/or trends which comprise the cascade, the severity ofthe perturbations and/or trends, the number of systems affected by thecascade, the presence, number and/or severity of failure of compensationin response to perturbations associated with the cascade, and the growthof the cascade to name a few. The processor may be programmed todetermine the rate of growth of the evolving cascade as by for exampleone or more of the increase in number and/or severity of newperturbations being added per unit time, and the increase number ofsystems affected, and/or the increase number of perturbations present indifferent systems, to name a few.

The processor may be programmed to detect the events or components whichare temporally and/or spatially associated with the cascade but whichare not part of the cascade for example treatment, surgical procedures,transport, injections, blood transfusion, sedation, IV assess,catheterization, manipulation, to name a few.

A processor-based system may characterize and quantify patientphysiological conditions by analyzing data relating the patient intotime series data and then generating an image or moving image of theabnormal components of the time-series that may be further processedinto operator-interpretable data. According to one embodiment, this maybe accomplished by generating a large set of time-series of datarelating to the physiologic system, converting the datasets (includingmonitored datasets, laboratory datasets, and historic datasets) intoparallel time series of each data component, separating the unperturbedtime-series components from the perturbed time-series components,aggregating the abnormal components into a real-time motion pictures ofthe abnormal components, and recognizing and interpreting the motionpictures and the events relational events and image components of themotion picture.

In one embodiment, data from the electronic medical records and patientmonitors are used to generate graphical displays, which may includemoving pictures of the patient condition. In an embodiment, such movingpictures, or animated displays, may be referred to as “motion picturesof physiologic condition” (MPPC). Provided herein is a processing systemand method for generating real-time MPPC of clinical data. The dataand/or images may also be analyzed to detect perturbations, aggregateand cascading perturbations, perturbation relationships, physiologicresponses to perturbations, treatments associated with theperturbations, physiologic responses to the treatments, physiologicfailures, testing failures, treatment failures, and communicationfailures to generate the MPPC. In addition, the MPPC may also include agraphical representation of any treatment applied in association withthe clinical condition.

Once the image or moving image (i.e. an image that includes more dataover time as the patient monitoring progresses) MPPC of the patientcondition has been generated, this image may be further processed tocreate an operator-interpretable indicator to assist in patientdiagnosis and/or treatment. For example, the image may be directlycompared to a database of similar images taken from patients withclinically confirmed diagnoses. The database image or composite ofmultiple images with the greatest similarity to the generated image mayindicate the correct diagnosis for the patient. For example, if thegenerated moving image, particularly as the image progresses over time,has the greatest similarity to a database image indicating “myocardialinfarction,” a processor may generate a text or other indicator to ahealthcare provider indicating such a diagnosis. The processor may alsoindicate that additional tests should be ordered to confirm thediagnosis. The processor may also indicate and/or provide orders forspecific treatments in light of the diagnosis. In an embodiment, amoving image may be indicative of two or more clinical conditions. Theprocessor may indicate tests that may rule out one or more of suchconditions. In addition, over time, one condition may be determined bythe processor to be more likely while additional time-series data mayalso rule out another condition.

These database images may be formed from retrospective clinical data. Inan embodiment, the images may be analyzed for similarity by any suitabletechnique, including image registration. In an embodiment, theindividual time-series objects that make up the image may be processedas a group for similarity to other groups of time-series objectsassociated with a particular diagnosis or clinical condition. The MPPCmay, for example, include abnormal and/or perturbed components and inparticular “Motion Pictures of Physiologic Failure” (MPPF) of thephysiologic system and of exogenous forces relating to that system.

Also provided herein is a processor and processing method for theautomatic generation and/or analysis of the images of physiologic and/orclinical condition and the characterization and aggregation of the imagecomponents of complex dynamic systems, such as physiologic systems andmedical care systems. The processing system may generate real-time MPPCof healthcare signals and processing those images to timely detectperturbations, aggregate and cascading perturbations, perturbationrelationships, physiologic responses to perturbations, treatmentsassociated with the perturbations, physiologic responses to thetreatments, physiologic failures, testing failures, treatment failures,and communication failures to generate and then recognize motionpictures of physiologic failures and of the treatment applied inassociation with the failures. According to one embodiment, a processorfirst renders parallel time-series from each of a plurality of sensorsand testing sources, which are applied to broadly monitor the dynamicsystem for failure. In an example, a processor programmed withinstructions for time series objectification of patient data detectspatterns along the parallel times-series, converts these patterns intotime series of discrete objects, then organizes these objects intodiscrete relational objects (such as binary objects, or relationalbinaries, derived of relational object pairs). The processor thenorganizes the relational binaries to render a unifying programmaticimage of the physiologic system and the care provided. The processorthen automatically recognizes objects in the image components and may beable to perform analysis on the images.

One embodiment may a patient safety processor having a single processoror a combination of processors programmed to generate time seriesobjects, a relational binaries, moving images, patient safety images,and/or patient safety visualizations. The patient safety processoroutputs images of the patient's physiologic system and medical care. Inan embodiment, the processor includes processing functions for timeseries objectification, relational binary processing, and an imagingprocessing. In an embodiment, the imaging processing includes a singlematrix construction processor.

According to an embodiment, perturbations detected by the processor areconverted to image components that may be used to generate a movingimage. In an embodiment, an MPPC may be representative of a “motionpicture of physiological failure” (MPPF) when a failure image becomesprogressively more complete and recognizable by the processor as eachadditional failure image component is added. One embodiment may involvebuilding a dynamic real-time image of disease, injury, and/or drugreactions, the care provided, and the expense associated with that care.The image is initially associated with initial image componentsincluding one or more minor perturbations, which may for example becaused by circulation of one or more toxic and/or immunogenic materialof endogenous or exogenous origin. At first these perturbations, such astoxins, inflammatory and/or thrombogenic mediators, may induce and/orcause only minor changes in cell permeability, ion flux, and triggervarious minor physiologic perturbations and responses each of which mayproduce an image component. The measurements of various mediators, ions,biologic profiles, as well as standard blood tests, and the outputs ofvital sign monitors may begin to vary as a function of these earlyphysiologic perturbations and responses, and it is these variations thatenlarge the group of image components from which the larger image (i.e.the MPPF) is derived. Early in the process, each of these alterations inpermeability, cell injury, mediator production, and physiologicperturbations, when considered in isolation, are often minor. However,collectively they may represent the early manifestations of a nascentand evolving moving image of a serious clinical condition.

According to one embodiment, each perturbation is programmaticallyorganized to form an image component of the MPPC. Many of these detectedimages components may be isolated because they are related to a benignprocess, and the image may self-extinguish or may not develop into animage associated with a clinical condition involving intervention or anMPPF. Yet, as noted above, others may represent the first imagecomponents of an early moving image. Provided herein are systems andmethods for the detection of the early image components of an evolvingmoving image to provide timely detection of physiologic failures beforethese failure progresses to shock (including, for example, hypovolemic,obstructive, septic, toxic, cardiogenic, hypoxic, and/or hypercarbicshock.) In one embodiment, it is advantageous to detect the early imagecomponents of the moving image before shock develops to improve theprognosis for the patient and to apply goal-directed therapy whileclinical intervention is still beneficial.

According to one embodiment, a patient safety processor generates aMPPC, which may be used for processor-based protocolization of care.This motion picture may be comprehensive of multiple data sources,including not only the events comprising a single or few parameters,such as heart rate, but also other parameters that may include, forexample: the slope and pattern of the heart rate, the slope and patternsof the systolic pressure variation, the slope and patterns respirationrate, the slope and patterns SPO₂, the slope and patternsventilation-oximetry index, the slope and patterns drug and fluidinfusion rate, the slope and patterns blood pressure, the slope andpatterns of the Neutrophil count, and the slope and patterns ofinflammatory and/or thrombotic markers, and various other blood, urineand/or exhaled gas test to name a few. The signals from all of thesesources may be converted to time-series and/or step functions and may,for example, be physiologic signals, therapy signals, laboratorysignals, or historical signals, which may be objectified, as by anobjectification processor, to produce the discrete programmatic objects(events). According to one embodiment, the processor detects a firstdiscrete event that includes a pattern or value of at least one medicalsignal, and a second discrete event that includes a second pattern orvalue of at least one medical signal, the processor then aggregates atleast the first event and the second event to produce a first relationalobject, the processor further detects a third event that includes apattern or value of at least one medical signal, and a fourth event thatincludes a second pattern or value of at least one medical signal, theprocessor then aggregates at least the third event and the fourth eventto produce a second relational object. The first relational object andthe second relational object are then aggregated to produce a firstimage component. Additional image components are built accordingly andthe image components are then aggregated according to the time ofoccurrence to derive the moving image and care.

In an example, the pulse related components of the typical motionpicture of sepsis failure cascade would include occurrences such asearly rise in heart rate, rise in pulse amplitude, and rise in slope ofthe pulse upstroke (as measured at the finger tip) in combination andtypically proceeded by a brisk rise in inflammatory markers. In contrastthe typical motion picture of occult hemorrhagic failure cascade (as forexample due to heparin related retroperitoneal hemorrhage) would includeoccurrences of an early rise in heart rate, a fall in pulse amplitude,and a fall in slope of the pulse upstroke (as measured at the fingertip) and a rise in the respiratory related pulse pressure variation anda fall in hemoglobin. According to one aspect of the present inventionall of these occurrences along the image of an occult hemorrhagicfailure cascade can all be derived from a multi wavelength pulseoximeter.

According to an embodiment, a relational binary processor is providedthat divides detected variations into discrete alpha events and betaevents, which are combined by the relational binary processor toconstruct the relational events which are termed relational binaries.These relational binaries are aggregated according to timing toconstruct image components. These image components are then furtheraggregated according to timing to construct and progressively build MPPC(from which visual images or electronic representations may be derivedas desired). These MPPC are often moving images of catastrophiccascading failures, thereby allowing more reliable detection to allowtimely rescue of the patient.

The signals may be chemical or physiologic measurements, as provided bypatient monitors, recorded in the electronic medical record, and/or maybe biomarkers specifically ordered, either automatically by theprocessor or manually by the clinician to indicate the potentialpresence of the sepsis (as those, for example, disclosed in U.S. patentapplication Ser. Nos. 10/704899, 11/647,689). The presence and/orconcentration of such markers may be presented in the context of theMPPC with the timed positioning relative to the others parameters, whichthen allows the relevance of the biomarker to be much more readilyidentified. According to an embodiment, the temporal and relationalpattern of inflammatory markers and temporal and relational patterns ofcontemporaneously measured or associated physiologic parameters areaggregated to produce a progressively enlarging MPPC of an evolvingpatient condition.

Therefore, to achieve the detection of various pre-shock states as wellas earlier detection of failures, one embodiment detects earlyvariations and aggregates them to provide an MPPC to dynamically presentexpanding failure cascades of pre-shock and shock states. This allowsseparation of expanding images from the smaller and less expansive imagecomponents having benign characteristics, and further allows separationof the images of minor isolated failures from failures that progress togenerate an expanding MPPC heralding the potential for transition to oneof the shock states. Each group of images as well as the complete MPPCand care may be analyzed for the purpose of assessing patient care in ahospital, a ward, or under the care of a given healthcare worker.

The occurrence of a large number of image components indicating noncascading failures which self extinguish may be indicative of anunstable patient population or poor health care delivery. In thealternative a large number of cascading failures are indicative of majorrisk of a high rate of death or injury in that environment or under thathealthcare workers care. The MPPC and the image components may be usedto determine if that is due to the patient population or the quality ofthe care.

One embodiment detects failure cascades along with the determination ofthe specific fundamental perturbations, or treatments, or lack oftreatments that occur early in a failure cascade. Specific fundamentalfailures are detected before they progresses to complex failures andparticularly before they progresses to the pre-shock or shock state.Furthermore, the processor builds an image derived of the relationalperturbations and treatments as the cascade expands. According to oneembodiment, each time series is processed to separate expected eventsfrom unexpected events. The unexpected and/or abnormal events are thenaggregated further to repetitively generate relational events, imagecomponents and finally the MPPC which comprises a motion picture of thecascade (if present) as well as the treatment applied in associationwith the cascade. This MPPC is further processed to allow the detectionof the probable cause or causes of the occurrence of the moving failureimages well as the image components of the MPPC as it evolves therebyallowing detection of the nature and cause of the failure cascade.

As noted above according to one embodiment, an analysis is providedwherein the fundamental components of the analytic process comprise abasic relational variable that includes a plurality of events. In apreferred embodiment, the basic relational variable is that includes twoevents (a relational pair) and this is called a relational binary. Inone embodiment, the relational binaries are initially selected by theusers as from a menu (or by a drag and drop interface) of relationalbinaries and/or of events from which the user builds the desired objectbinaries the binaries are then used as by drag and drop to build theimage components and MPPC for detection. This may be performed by, forexample, by national or regional expert groups, or by specificdepartments in a hospital, or by an individual physician to providecustom management. This may also be automatically performed by theprocessor (as, for example, through the investigation of a large numberof historical data sets that have been comprehensively analyzed andcategorized according to outcomes. The objectified time series matrixand/or the MPPC may be may be outputted in various interactive,hierarchical, and relational formats for review and automatic or manualadjustment.

The MPPC may detect a wide range of failures. For example: “physiologicfailures, treatment occurrence failures indicating the absence ofexpected treatment in relation to a given perturbation, testingoccurrence failures indicating the absence of expected testing inrelation to a given perturbation, treatment response failures indicatingthe absence of the expected correction of perturbation or the occurrenceof a new potentially complicating perturbation in relation to a giventreatment and/or dose.

The processor combines the complex data of the electronic medical recordinto a single motion picture of perturbations, treatments, physiologicresponses, diagnostic testing, recoveries, diagnoses, missing data,patient locations, and/or other datasets. Dynamic images are generatedof relational variations of a set of time series associated with acomplex system to generate a real time motion picture of a failure ofthe system and/or of forces applied to the system. According to oneembodiment of the present invention, the patient safety processorautomatically outputs a unified timeline, for example, derived ofdetected images of a given type. According to another embodiment of theinvention the processor, upon detecting a failure cascade, may presentand highlight the evolving MPPC in real time on an outputted display ofan image diagram for the physician to review. The portion of the motionpicture, which has already been completed, may be reviewed backward andforward to review in a single summary snap shot view.

In one embodiment, an electronic medical record may be converted to anMPPC. A patient data processing system comprising a processor programmedwith instructions for converting an electronic medical record into trenddata, such as sequential, timed data, for example trends of physiologicparameters and laboratory data over time. When the data is convertedinto trend data, the processor may detect relationships between thetrend data. For example, such relationships may include positive and/ornegative trends. The relationships may be relational trends (i.e., whenone parameter goes up, another parameter goes down). Complex cascadepatterns of physiological conditions may be formed from a plurality ofcombinations of relational trends. The complex casade may form an MPPC,for example of sepsis, severe sepsis, septic shock, and microcirculatoryfailure, a shock cascade, and a septic shock cascade.

For example, a processor may process the electronic medical record andsearch to detect sequential and timed trends including positive andnegative trends of physiologic parameters and laboratory data. Then, theprocessor may determine relational timing of the detected positive andnegative trends to detect a complex cascade patterns that include aplurality of combinations of positive and negative trends evolving insequential timed relation to each other. The processor may output anindication of the detected complex relational cascade pattern. Forexample, the indication may be physiologic failure, such as sepsis,severe sepsis, septic shock, and microcirculatory failure, a shockcascade, and a septic shock cascade. The processor may also providedetailed information about the individual trends, such as the length ofeach trend or the timing of the entire cascade. If therapy informationis included in the electronic medical record, the image may include anindication to mark the onset of therapy and to determine and output atleast an indication of timing of therapy in relation to the cascade. Ifthe electronic medical record is from a patient that is still in care,the processor may include an alarm functionality to indication earlypoints in a failure cascade.

In another embodiment, a patient data processing system may identifyspecific positive and negative trends, such as a combination ofinflammatory trends, metabolic trends, hemodynamic trends, hematologictrends, and respiratory trends. After the identification of the trends,the processor may identify the relational timing of positive andnegative trends, which relationally or collectively are indicative ofthe septic shock or pre-septic shock failure cascade to identify andoutput an indication of the septic shock or pre-septic shock failurecascade.

During a failure cascade, the earliest point in the cascade may includean earliest trend (e.g., a respiratory, immunologic, hemodynamic, orother patient trend) that marks the beginning of the cascade. Therapyintervention at this point may have the highest chance of success. Inone embodiment, a processor may analyze the relational pattern toidentify the earliest trend of a component of the cascade, identify theonset of treatment, and identify the timing of treatment in relation tosaid earliest trend. Such analysis may benefit caregivers in determiningwhich therapies have the highest success rate for a particularphysiological condition cascade. Alternatively, such information mayalso help a physician determine which types of cascades are likely to beself-limiting.

Many physiologic failures such as, for example septic shock, pulmonaryembolism, congestive heart failure, respiratory arrest due to narcoticsin the presence of sleep apnea, thrombotic thrombocytopenia purpura(TTP), hemorrhage due to anticoagulation, respiratory failure due tobronchospasm, and adult respiratory distress syndrome, but not limitedto these clinical conditions, begin with one or two non-specificperturbation(s). Physiologic failure is commonly a relational expansion,often beginning with a fundamental physiologic perturbation at a singlefocal point in time. In fact, this initial perturbation is oftencompletely masked once the cascade has progressed past a certain point.In such cases, testing or monitoring for the single perturbation may notbe useful for making a diagnosis. In many cascading clinical conditions,the first perturbation(s) of the cascade may often only be detected inretrospect after the cascade has further progressed when the firstperturbation(s) is no longer present. This provides a basis foroptimizing the detection of the first point(s) by real-time imaging ofthe cascade as it develops and then examining the image to determine thefirst perturbation(s).

While a pattern of a single time series provides a larger image of adynamic process than a single value or range, such a pattern is stillonly a tiny image fragment of the process. The determination ofthresholds and even the detection of various patterns of perturbationscomprise incomplete analysis, which will inevitably allow anunacceptable rate of progression to catastrophic failure. Even insituations wherein a measurement or test may seem definitive as astand-alone test, action or conclusions based on a single value (or anaverage of a plurality of values) will have a reasonable probability ofbeing incorrect. Consider, for example, a single measured spot SPO₂value of 94. This value is largely meaningless without knowing if theSPO₂ is rising, falling, or cycling. Yet this infinitesimal imagefragment of a patient's complex physiologic system is used everyday inhospitals to determine care. Furthermore, even if the pattern of theSPO₂ is known (for example the SPO₂ has been stable at about 94 for atleast 12 hours) this is an incomplete image, which is largely uselessand, in fact, a potentially misleading piece of information. Withoutknowing the relational pattern of the minute ventilation during therelated time interval of the measured SPO₂ pattern, the healthcareworker may be lulled into a false sense of security even as the patientis dying of septic shock or heart failure. Furthermore, an alarm orinterpretive output which is based on a programmatic image of both thepatterns of both the SPO₂ and the related minute ventilation withoutadditional relational elements of the image, such as, for example, theassociated pattern of the white blood cell count, temperature, pulse,blood pressure, microbiologic values, and medications will be incompleteleaving too much synthesis for the healthcare worker. In anotherexample, consider the detection of a pattern of a sustained rise inpulse or respiration rate. Each such pattern represents a tiny fragmentof the present physiologic state and each pattern may be benign oralternatively may be an early image component of a much larger dynamicprocess of failure often associated with an evolving failure cascade.The difference between a benign or pathologic rise in pulse orrespiration rate cannot be determined with this tiny image alone andoften cannot even be known at the time of the onset of the rise.Therefore a tree diagram protocol with a branch based on a rising pulseor rising respiration rate adds a great degree of programmaticcomplexity with a high risk that the protocol will precede down thewrong pathway.

As noted above when the detection and the determination of the mode ofpotentially fatal but profoundly complex physiologic failures is left toa population of heterogeneous healthcare workers, an unacceptable rateof death may be anticipated.

As well, an incomplete analysis of the physiologic system will oftencause the healthcare worker to generate a large amount of investigation,testing, analysis and evaluation that is not necessary and thereforeincreases the cost of overall care. Further, these false paths oftreatment and evaluation may distract the care worker from thedetermining the actual operative failure modes, which will ultimatelyinduce adverse outcomes.

Prior to shock, a patient's physiologic system is perturbed by bothdisease and treatment. A given treatment provided to correct aperturbation might reduce the perturbation, have no effect on theperturbation, exacerbate the perturbation, cause another perturbationand/or make another perturbation worse or better. To determine whicheffect a treatment is having and to assure that this determination oftreatment effect is complete, it is necessary to collect and, just asimportantly, as provided by one embodiment, organize and analyze largeamounts of relational data in a timely manner.

Another problem is that, within present hospital systems the healthcareworker is forced to do a great deal of archeology (digging, isolating,identifying, etc.) before synthesis may be effectively completed. Forthis reason, the synthesis of information by the healthcare worker isoften not executed in a manner, which allows immediate searching,filtering, re-analysis, etc. This friction combined with the typicalworkload of healthcare workers limits the number and range of high-levelscenarios, which may be investigated. Also the healthcare worker may,because of lack of available organized data and time, execute decisionswithout a complete set of synthesized information and worse, may notrealize that this is the case.

For these reasons, even with conventional electronic medical recordembedded protocols, patients remain subject to a range of failuresacross a broad range of failure modes based on the complexity of theirindividual condition and the complexity of the environment facing thecare giver. In fact, because failures often overlap, one protocol mayreduce the risk of one failure while increasing the risk of another. Forexample, oxygen given to treat hypoxemia under one protocol may delaythe detection of pulmonary embolism by stabilizing the SPO₂ and hidingthe early signs of impending shock from the healthcare worker.

Although the number of potential modes of failure is very high in anyhospital environment, the occurrence of certain modes of failure isreasonably likely under a given set of circumstances in the hospital. Afailure mode diagram illustrating common modes of failure given acombination of a group of diseases is shown in FIG. 1. The number ofpotential failures may be very large (in the hundreds) for a givenpatient in a hospital setting and the nurse or physician is oftenexpected to monitor many such patients on the floor while timelydetecting the failures such that the nurse is expected to timely detecteven a single failure from as many as a thousand failures which mayoccur among the patients under his or her care. For this reason,processor based failure imaging and detection is desirable.

FIG. 1 illustrates a complexity diagram 200 of an exemplary patient on amedical hospital ward. The diagram 200 demonstrates the level ofcomplexity that may be modeled into moving images as provided herein todetermine the nature of and origin of perturbations within this level ofcomplexity. The diagram 200 is one type of failure mode diagram whichmay be constructed by an expert panel and then used according to oneembodiment to facilitate the construction of the various components themoving images provided herein, including the events, relationalbinaries, and image components. The failure image component diagram 200includes a number of overlapping diseases present for this singlepatient including diabetes 202, congestive heart failure 204, arterialfibrillation 206, stroke 208, sleep apnea 210 and sepsis 212. Thediseases may induce physiologic failures, such as a divergent rise inventilation 216, a rapid ventricular rate 218, pulmonary edema 214, andfall in oxygen saturation (hypoxemia) 222. Furthermore the treatmentsare potentially associated with medication failures such as a highthreshold breach of the partial thromboplastin time (PTT) or a lowthreshold breach of the glucose (hypoglycemia) 234. Additionally, theadministration of a treatment (for example, insulin 224, a diuretic 226,an ACE inhibitor 228, a beta blocker 230 and/or heparin 232) to apatient may lead to additional physiologic failures (for example, a fallin platelet count (thrombocytopenia) 236, the occurrence of heart block238, a fall in serum potassium (hypokalemia) 240, a fall in serum sodium(hyponatremia) 242, a fall in blood pressure (hypotension) 244. In oneembodiment, a single patient may have early high blood glucose(hyperglycemia) 215 followed by later low blood glucose (hypoglycemia)234. As shown, the interrelationship of progression of multiplediseases, the patient symptoms, and multiple treatments may lead totreatment delay 248 or confusion 220.

FIG. 2 depicts an overview of the flow of analysis for modeling complexpatient physiological condition in one embodiment. A wide range sourcesmay provide inputs to the modeling. For example, patient monitors 256,patient records 272, historical patient data 260, lab results 264 andtherapy data 268 may provide the raw data input into the analysisstream. These inputs are converted to a set of parallel time series 276.Patterns and threshold violations along this plurality of parallel timeseries identified, coalesced, synthesized and organized into discreteobjects forming object streams 280 within each channel. These discreteobjects are analyzed to identify known relational patterns intoinstances of relational binaries 284. In one embodiment, expert systemsthen further refine the analysis by organizing and synthesizing theserelational binaries into a set of failure images 288, which as anaggregate whole make up a unified programmatic image of the complex anddynamic state of a patient and/or a patient population.

FIG. 2 depicts the flow of analysis 240 from raw data to the aggregateof images, while FIG. 3A and FIG. 3B includes some of the data stores,data flow, processors and output mechanisms within the exemplaryembodiment. FIG. 3A depicts another data flow of one embodiment. Thedata management system 300 includes a monitor 302, a processor 304 thatmay include, for example, time series objectification processor 336,relational binary processor 348, and failure imaging processor 360.Alternatively, processors 336, 348, and 360 or instructions forperforming the processing steps of time series objectification,relational binary processing, and/or failure image processing may belocated on one or more additional processing components in communicationwith processor 304 that are part of the system 300. The processor 304 isadapted to provide output of the analysis to a device 306, whichprovides an interface for a healthcare worker. The data flow involvesinputs from a wide range of sources (304, 308, 310, 312, and 314). Asshown, the inputs may be sent to a processor 304 that may direct furtheraction for the patient, including testing orders 316, indicators to thehealthcare provider that may be displayed on a console or device 306,and therapy orders 315. Accordingly, the healthcare worker may use thedevice 306 to control and oversee the entire hospitalization process. Inone exemplary embodiment, the processor 304 may be used to drive thedevice 306. The processor 304 may be adapted to constantly process allof the real-time data of all of the patients regardless of the status ofthe viewing console and to automatically send testing orders 316 and/ortherapy orders 315 based on the analysis of the images derived from theprocessor 304, as will be discussed.

The data management system 300 may include one or more processor-basedcomponents, such as general purpose or application-specific computers.In addition to the processor-based components, the data managementsystem 300 may include various memory and/or storage componentsincluding magnetic and optical mass storage devices and/or internalmemory, such as RAM chips. The memory and/or storage components may beused for storing programs and routines for performing the techniquesdescribed herein that are executed by the processor 304 or by associatedcomponents of the data management system 300. Alternatively, theprograms and routines may be stored on a computer accessible storagemedium and/or memory remote from the data management system 300 butaccessible by network and/or communication interfaces present on thecomputer.

The data management system 300 may also comprise various input/output(I/O) interfaces, as well as various network or communicationinterfaces. The various I/O interfaces may allow communication with userinterface devices, such as a display, keyboard, mouse, and printer thatmay be used for viewing and inputting configuration information and/orfor operating the system 300. The various network and communicationinterfaces may allow connection to both local and wide area intranetsand storage networks as well as the Internet. The various I/O andcommunication interfaces may utilize wires, lines, or suitable wirelessinterfaces, as appropriate or desired.

In an exemplary embodiment, the device 306 is turned on as forcontinuous viewing (with a notification) by the processor 304 whenimages are indicative of a significant potential failure and/or cascadeprocess or at a point wherein the patient's risk class exceeds athreshold value. The risk class may, for example, be derived as afunction of a calculated instability index or a detected instabilityindex pattern and/or detected failures. The instability index may be,for example, a confidence metric correlated with a matched image. Forexample, when an MPPC has a high likelihood of being associated with aserious condition, the instability index may be high. The instabilityindex may be a numeric index, a color or graphic indicator, and/or anaudio or text message.

In accordance with an exemplary embodiment, the device 306 includes aninteractive single screen displaying items, such as one or more workingdiagnoses, differential diagnosis, parameters derived from patientsincluding laboratory parameters, monitored parameters, and subjectiveparameters (e.g., sedation scale, confusion scale, or pain scale) or thelike. In an embodiment, the term “parameter” herein may refer to anabsolute or relative data point or set, a pattern, or a deviation, arange of such data points or sets, a pattern of such data, arelationship along a single set of data and/or or between a plurality ofsets of data, and/or patterns of data. The data may be an objective datatype or subjective data type and may be directly and/or indirectlyderived or historical in origin. In addition various outputs from thefailure imaging processor 360 (FIG. 3B) may be displayed. According toon embodiment, the processor 304 may provide data for display present onthe device 306 or through a report (either electronic or paper) orwithin an electronic representation that may provide an interface toexternal systems.

The data management system 300 further includes a medical recordsdatabase 308 including laboratory data 310, historical data (e.g.,diagnosis) 312 and therapy data (e.g., medications) 314. The medicalrecords database 308 is coupled to the processor 304 and to the monitor302 so that those systems may have access the data stored in the medicalrecords database 308. The processor 304 may include a component ordirect link to the centralized patient medical record, which containsreal time data and receives data input from all hospital sources. Thus,a database containing substantially all of the components relating tothe patient available to the hospital may be directly accessible to theprocessor 304 in real time to allow the embedded relational processorrender relational binaries, and construct and detect failure imagecomponents which include these data from varied sources.

In accordance with an exemplary embodiment, the processor 304 is adaptedto comprehensively engage the medical records database 308. As discussedfurther below, the processor 304 may be programmed to provide forformal, automatic simultaneous engagement, of physiologic failure imagecomponents, medication failure image components, testing failure imagecomponents, aggregate failure image components as derived from therelational processor and to render them in a timeline for viewing.

The processor 304 may be adapted to provide an immediate review of allfailure image components and to take action based on the detection ofspecific failure image components. The processor 304 may be capable ofresponding faster and more reliably than the healthcare worker becauseit may be adapted to constantly monitor the evolving failure imagecomponents form the earliest onset of the first divergent binary. Theprocessor 304 may therefore detect failure image component cascades,which originate from single divergent binaries, which might easily beundetected by the healthcare worker until it is too late. The processor304 may also be programmed to alarm on divergent or null binaries uponwhich no action has been taken or upon which the action has notcorrected the evolving divergent binary or failure image component. Forexample, in a scenario in which the processor 304 has been updated bythe nurse that a blood culture has been obtained, the presence of a nullbinary may be generated indicating testing failure image component ifafter a pre-selected time the result is not available to the processor304 whereas the presence of a divergent binary indicative of aphysiologic failure image component may be detected if the culture ispositive. If testing failure image component is detected the processor304 notifies the lab of the apparent delay. The notification is an alphaevent and a receipt response to that notification is a true beta event.Therefore the failure of the lab to indicate receipt may cause theoccurrence of a divergent binary, which may trigger the notification ofthe nurse in the same manner until a convergent binary concludes thesequence. If on the other hand, a physiologic failure image component isdetected (the culture is positive), the processor 304 notifies the nurseagain in the same binary generating fashion.

While a positive blood culture is the beta event of the culture testingbinary, it is the alpha event for another group of testing binaries suchthat the initial divergent testing binary may cause the processor toassure acquisition of a complete blood count, a comprehensive metabolicprofile, increased frequency of blood pressure and pulse measurements,ventilation indexing oximetry and other testing as programmed into theprocessor 304 in response to the specific divergent binary detected (inthis case a positive blood culture). These new testing binaries maygenerate unexpected beta events (such as a low blood pressure, a highpulse, or high ventilation to oximetry index) and these beta events maythereby define a new set of divergent physiologic binaries. This new setof divergent binaries (in aggregation) may be sufficient to meet thepre-selected criteria of an aggregate failure image component suggestiveof early septic shock, which diagnostic consideration now comprises analpha event to a plurality of new binaries which have been programmedinto the processor to assure timely and proper monitoring, timely properpatient location, and timely proper diagnostic testing, and timely andproper intervention in the event of the detection of this type ofaggregate failure image component. In addition, the beta events of thedivergent physiologic binaries which comprised the aggregate failureimage component now become alpha events for new physiologic binarieswherein the beta event of each of the new binaries comprises the returnof each these values back to a normal range within a pre-selected timeperiod (thereby assuring, that the aggregate failure image component iscorrected timely, if possible). In additional, the positive bloodculture is also the alpha event for a treatment binary such that theprocessor 304 may be expecting to see the correct antibiotic in responseto positive blood culture administered within a pre-selected timeinterval. If this does not occur a divergent binary indicating treatmentfailure may be identified and assured nurse notification may proceed bythe binary building method previously discussed.

According to one embodiment, in response to the detection of anysignificant divergent physiologic binary, the device 306 may beprogrammed to prevent the failure of notification by building a setnotification binaries, which must end with convergence. The device 306may also be programmed to prevent failure to timely treat by building aset of treatment binaries, which must end with convergence. Further, thedevice 306 may be programmed to prevent failure test by building a setof testing binaries, which must end with convergence. The device 306 mayalso be programmed to detect associated physiologic failure imagecomponents by identifying divergent physiologic binaries in associatedwith the initially discovered divergent binaries.

According to one embodiment, the PSP includes an associated, connectedand/or embedded eventing system. In this eventing subsystem, users maydesignate actions to be initiated or data to be recorded when a specificoccurrence is identified. This eventing system may interface with otherinternal or external systems including notification systems, workflowsystems, asynchronous communication systems, reporting systems, decisionsupport systems, dashboards, data warehousing and/or data mining systemsto name a few.

According to one embodiment the relational processor is self-modulatingand provides an automatically expanding analysis, which is rapidlyresponsive to the occurrence of even a minor failure image component.The analytic activity of the processing system is capable ofmultidimensional growth and diminishment in direct response to themagnitude and number of failure image components detected. In thisregard, the processor 304 upon the occurrence of a physiologic failureimage component may generate a cascade of notification, testing,treatment, and physiologic binaries even if that failure image componentcomprises only a single physiologic divergent binary. The beta event ofthe physiologic binary may comprise the alpha event of each of a newgeneration of notification, testing, treatment, and physiologicbinaries. Each of these new binaries also have a beta event, each whichmay induce the formation of other binaries wherein the beta eventcomprises the alpha of another binary of the same or another type. Aspontaneously growing cascade of binaries thereby evolves towardassuring timely notification, timely testing, and timely restoration ofphysiologic stability.

A rapidly expanding, cascade of these types of divergent binariesindicates evolving patient instability of the patient or poorperformance of the healthcare system. An analysis (as by objectifiedpattern recognition or statistical analysis) of the timed patterns ofthe types and sequence of the divergent binaries may allow thedetermination of poor health or poor responsiveness of the healthcareworker is causing the cascade to be propagated. As health is restored,and provided the healthcare workers are timely responsive, the binarycascade may automatically diminish and the various failure imagecomponents may no longer be detected. The outputs of the relationalbinary object processor therefore provides a self modulating processingsystem which may be readily used and further analyzed to track thehealth of a single patient, or the patients on a given floor, or thepatients hospital wide. The outputs of the object binary processor alsoprovides a self modulating processing system indicative of the qualityof healthcare delivery provided to a given patient, on a given floor, orhospital wide.

The processor 304 may be applied to other complex dynamic data setsother than medical data wherein a self-modulating relational analysisand control would be useful. The processor 304 has utility for the datamining, for example in association with the processing of archiveddatasets to identify the failure image component process from theinitial spark (the first divergent binary) to extensive system failure.The processing of archived datasets provides the opportunity to reviewthe automatic modulation of the binary cascades which are derived ofvarious failures and to facilitate the construction of dynamic failureimage component diagrams for complex processes in the hospital, as wellas in industrial processing such as the food, chemical, orpharmaceutical processing. The processor may be programmed such that theuser may select each alpha event and allow the processor to detect,offer, and/or derive events and relational binaries, which havespecified temporal, frequency, or spatial relationships with theselected event object. Alternatively the processor 304 may be programmedto construct its own set of convergent object binaries with a learningdataset by processing the outputs of healthy individuals and then theprocessor may be used to detect divergent binaries when applied topatients by identifying the lack of the expected beta events (which weredefined by the learning dataset). Sensitivity for cascading (theinitiation of further processing based on the detection of a divergenceor a failure image component) may be adjusted by modifying thesensitivity for trueness of the beta event or by modifying the criteriasuch as slope, or magnitude of the objects during the objectificationprocess. This provides a high degree of flexibility in definingsensitivity to the designation of a binary as divergent and thistherefore allows a high degree of control over the sensitivity tocascade initiation, propagation, and extinguishment. Cascades may bemodular or divergent or failure image component specific. A modulargroup of cascades may be selectable from a menu and then each one in thegroup modified as desired.

As shown in FIG. 3B, the processor 304 may include instructions for anynumber of processing functions. As shown the processor 304 may includean event editor 331 (creates event definitions 332), a convergenceeditor 343 (creates binary definitions sets 344), and a failure imagecomponent 355 (creates failure components 356). The event definitions332, binary definitions 344, and failure components 356, may be used aninputs for the time series objectification processor 336, the relationalbinary processor 348, and the failure imaging processor 360. The timeseries Objectification Processor 336 is programmed, with the rules andparameters provided by the event definition set 332, to convert paralleltime series (324, 328) of the electronic medical record 320. Therelational binary processor 348 then, with the rules and parametersprovided by the binary definition set, processes the object streams 340to generate streams and cascades of relational binaries 352. Furtherthen, the failure imaging processor 360, with the rules and parametersprovided by the Failure image component definition set 356, synthesizesthe relational binaries, and in some cases isolated objects from theobject stream, into one or more images 364. The output of each of thesethree processors (336, 348 and 360) as well as the original time seriesupon which they were applied are stored in an MPPC database 368. In anexample, the processor 304 may be programmed so that detection of one ormore events, binaries, image components or detection of a specific MPPC,may cause the processor to take action such as provide an outboundnotification of the detection, orders for testing or treatment, ordirect control signals to a treatment and/or testing device to change,cease or initiate testing and/or treatment.

According to one embodiment, the relational binary processor 348 and thetime series objectification processor 336 may adapt to the output ofeach other to modify the analysis. For example, the detection of anevent, a reciprocation, an incomplete reciprocation or other objects orpatterns by the time series objectification processor 336 may cause anadjustment to the cascade responsive to the detection of a divergence.Alternatively or in combination the criteria for designation of a wavesegment as an event object within the time series objectificationprocessor 336 (for example the slope criteria for identifying a fallevent object of serum sodium) may also be adjusted based on the presenceof a specific alpha event. In an example, when an alpha event comprisinga diagnosis of cerebral vascular infarction (CVA) is detected, this maycause the time series objectification processor 336 to reduce theabsolute slope (less negative slope) for designating a fall event objectof serum sodium, which, is preferably one of the betas in such patients.By automatically reducing the absolute slope for the designation of thebeta event the alpha diagnosis of cerebral vascular infarction isadjusting the sensitivity of the diagnostic process allowing automaticand dynamic adjustment upon the occurrence and detection of differentphysiologic vulnerabilities. In this example, the increase insensitivity for detection of a fall event object in serum sodium (which,combined with the alpha that includes a CVA diagnosis) would comprise adivergent binary), which may trigger a diagnostic cascade for closemonitoring of the serum sodium and/or the evaluation of additionallaboratory studies and/or the reduction of free water delivery. This isdesirable due to the unique vulnerability faced by patients with CVA asa function of the potential for inappropriate increase in anti-diuretichormone due to the CVA.

Since the relational binary definitions within the binary definition set344 may be individually defined and refined by processing largepopulations of historical data, correlations may be verified, ratherthan being simply proposed and maintained as a function of consensus orexpert opinion. In one embodiment, cascades originated by criteria fordivergence provided by an expert, which untimely lead to extinguishmentwithout intervention may be automatically adapted to either change thesensitivity for the detection of the divergent beta or to change thecascade resulting for the divergent binary. In another example, cascadesoriginated by criteria provided by an expert which continue selfpropagate and expand despite timely action and without progression ofthe physiologic divergence may be automatically adapted to either changethe sensitivity for the detection of the divergent beta or to change thecascade resulting for the divergent binary. The sensitively andspecificity may be further enhanced because the system may be applied toarchived training data sets wherein the outcomes are known so themagnitude and direction of the cascades may be compared to the desiredmagnitude and direction of the cascades and adjusted accordingly. Withapplied archived datasets the application of auto-adaptive adjustment inevent criteria, divergence criteria, or cascade generation may beapplied until the cascades proceed without premature auto extinguishmentand excessive propagation. Furthermore the system may be applied tohypotheticals on the missing data to allow determination as to how theymight affect incomplete (null) binaries.

According to one embodiment the processors, including the time seriesobjectification processor 336, the relational binary processor 348 andfailure imaging processor 360, may output the results of their analysisinto the MPPC Database 368. The MPPC Database 368 contains the timeseries 328 on which the analysis was performed as well as the results ofanalysis including the event streams 340, the relational pairs 352, theaggregate failures 364 as well as aggregations, relationships andalternative images of these elements. In one embodiment, the metadatarule-sets (both primary and alternative and/or temporarily overridden oraltered elements) are persisted as XML (Event Definition Set 332, BinaryDefinition Set 344, Failure image component Definition Set 356) in thePatient Safety Image Database 368.

Time Series Objectification Processor

A time series objectification processor 336 may contain instructions asprovided in U.S. patent application Ser. Nos. 11/280,559, and 11/351,449the specifications of which are incorporated by reference herein intheir entirety for all purposes. Accordingly, such processors mayfunction by constructing a time series of each parameter derived duringthe process of the hospitalization and then objectify each time series.These time series may, for example, include objective measured values,drug dosing, infusion rates, and subjective clinical scores to name afew. At least some of the time series may be provided as a stepfunction. For example, time series of the weights, serum sodium values,SPO₂, respiratory rate, heart rate, drug infusion dose, sedation score,pain score, stupor score, working diagnoses, an instability score, aseverity of illness score, to name a few, may all be included. Fromthese time series, the time series objectification processor may renderan aggregate “object cylinder” or time series matrix for example, whichmay include parallel streams of objects derived from all of these timeseries.

In an embodiment, a time series objectification processor converts a setof time series into a stream of sequential and overlapping discreetelements or objects such that substantially the entire time series ofdata is converted to a time series of objects in a relational hierarchyof ascending complexity. The objects into which the time series isconverted may be predefined by the user and/or adaptively defined. Thediscrete objects which are created represent and characterize anoccurrence providing a time location and a set of properties derivedfrom the aggregated data within the boundary defined. This process whenapplied to a plurality of parallel time series generates an ObjectifiedTime-series Matrix (OTM). Objects may be very simple such as a briefrise or fall along a single time series, or highly complex such as asepsis cascade object comprised of and inheriting hundreds of simplerobjects of relational physiologic variation, treatment, and response totreatment to name a few across a large OTM. These objects along the OTMare differentiated by location and the properties derived and thereforeindividual objects can be qualified and the objects of the OTM can besearched against. The conversion of the time series matrix to form anOTM provides for identification, qualification and search ability ofrelationships between substantially all patterns and relationships whichis embodied in the data of the EMR. Objectification is therefore onemeans of converting an electronic medical records into a particularformat for imaging or searching for images. The objectificationprocessor may for example be programmed as a continuous search engine tocontinuously search for predetermined complex objects which at thislevel of complexity comprise images (such as the image of an evolvingsepsis cascades) along both the vertical and horizontal dimension acrossmultiple parallel time series of the OTM). When the electronic medicalrecords (EMR) of a patient is converted into an OTM, the continuoussearch engine may linked to an alarm processor to thereby provide anautomatic alert upon detection of specified images (such as the image ofa sepsis cascade, the image of failed or missed treatment, or the imageof a drug reaction to name a few). This converts the EMR into a realtime image generator with real-time detection of both complex and simplefailures. The wide range of simple and complex relational patterns orimages which are provided in an inheritance hierarchy of ascendingcomplexity and are continuously searchable are derived of for examplephysiologic process, pathophysiologic failure, and the care of thepatient, to name a few, are all exposed for continuous or intermittentsearching or imaging along the OTM

As discussed, one embodiment includes a patient safety processingsystem, which includes a time series objectification processor 336 and arelational binary processor 348. The relational binary processor 348 maybe embedded into, or communicate with the time series objectificationprocessor 336. The time series objectification processor 336 isprogrammed to convert parallel time series of the electronic medicalrecords from a central source or a wide range of sources as well as fromother processors (e.g., the Patient safety processor), into parallelobject streams. The relational binary processor 348 then processes theobject streams to generate streams and cascades of relational binaries.According to one embodiment, the processor 304 automatically outputs aunified timeline, for example, derived of detected failure imagecomponents of a given type. According to another embodiment theprocessor, upon detecting a failure cascade, may present and highlightthe evolving MPPC in real time on an outputted display of a failureimage component diagram for the physician to review. According toanother embodiment, the processor 304 persists failure image componentsand all other results of the analysis into the MPPC Image Database 368,which may be the source for visualization, reporting, and interfacesinto other systems. The portion of the motion picture which has alreadybeen completed may be reviewed backward and forward to review in asingle summary snap shot view.

As discussed, according to one embodiment, the relational binaryprocessor 348 generates relational binaries. Such relational binariesinclude an alpha event object and a beta event object. An early step inthis process includes the defining the relational binaries by the useror by the processor. To define a relational binary, first, the alphaevent is defined (as by the user or adaptively). The alpha event isdefined both in terms of its channel and the object along the channel.In one embodiment, the objects along each channel are defined bycharacteristics (such as the slope, amplitude, or other featuresdefining the object as discussed in the aforementioned patentapplications). Alternatively, threshold violations may be identified asan alpha event. A beta event is defined, again in terms of its channeland its characteristics and may be either a pattern or a thresholdevent. Both alpha and beta events may also be defined in terms of therelationship of its characteristics to the characteristics of otherevents, such as those, which preceded the specific event. In oneembodiment the user may define the relational objects, (as by using adrag and drop designer), by selecting the channel (which defines thetime series type), and by selecting the event objects (for example afall event or a rise event) which meet specified range of criteria, andby identifying the timed relationship (such as the time interval) of thebeta event in relation to at least a portion of the preceding alphaevent, and/or by identifying the spatial relationships and/or frequencyrelationships of one event to the other event. In one embodiment, alphaobjects and beta objects are defined by the criteria provided to thetime series objectification processor 336 alone for the detection ofevent objects such that the Relational Binary Processor may be concernedonly with the detecting the presence and timing of detected eventobjects not with modifying or affecting the criteria for event detectionif desired. (The detection of event objects by a time seriesobjectification processor 336 may be as disclosed in U.S. patentapplication Ser. No. 11/280,559 and U.S. Pat. No. 7,081,095, thespecifications which are incorporated by reference in their entiretyherein for all purposes.) This is not to limit the functionality of therelational binary processor 348 (since processing systems, whichincorporate the programming of the relational binary processor 348 tospecify criteria, are included in an embodiment,) to detect objects as afunction of basic time series patterns by the objectification processeswhere these basic patterns are converted to discrete objects. Therelational binary processor 348 then aggregates the relational binariesaccording to their time of occurrence and/or to specific criteria foraggregation set by the user or processor to derive image components andthe image components are aggregated according to their time ofoccurrence and/or to specific criteria for aggregation set by the useror processor to derive the MPPC and care derived of events and patternsacross hundreds of parallel time series. In a sense, the relationalbinaries and events become the discrete “pixels” from which MPPC of apatient's physiologic system are constructed by the processor 304.

According to one embodiment, the processor 304 or is also programmed toorganize the events and relational binaries into larger aggregatefactorable objects, which may also be constructed as a unified objecttimeline rather than a motion picture. Each aggregate factorable objectincludes a specific aggregation of events and relational binariesobjects. In some aggregate factorable objects, the individual relationalbinary and event objects occur in a specific sequence or range ofsequences (which may be overlapping) and the objects have a specifictemporal relationship (or range of temporal relationships) with respectto each other. One specific type of object timeline may be specified assimply a grouped set. In another example, relational binaries areordered in specified sequence in which the event and relational binariesobjects were detected thereby defining the object timeline.

According to one embodiment objects of specified types may also becombined derived to render a “unified patient timeline” which is asimple summary of the patient's physiologic system and care. The MPPCand care provides the information at more comprehensive level. Both maybe configured to provide further simplified summarization or imagedetail revealing drill down. The unified patient timeline may forexample, represents an instance of at least one factorable aggregateobject derived from a plurality of parallel time series into a singletime-series or time line, often of relational binary objects of aspecific type or plurality of types. In one instance the unified patienttimeline and/or the MPPC and care is constructed to be a life long timeline and/or motion picture, which preferably is recorded wheneversignals are available, such as during a hospitalization or whenconnected to a home monitor or when blood testing is made. The beginningof the motion picture or time line is defined by the time of theearliest date of data (which may be derived from archived patient data)the unified patient timeline does not end until a patient dies. Segmentsof the timeline (or motion picture)may be separated for examination bylocation of the patient such as a hospitalization segment, or by actionstaken to treat the patient, such as a peri-operative segment, or byevents relating to altered patients states such as the segmentimmediately preceding death or while sleeping. According to oneembodiment, an object nomenclature is provided which designates thetimed and sequence relationships of the binary objects and events of aplurality the parallel patient related time series, thereby converting alarge plurality of datasets into this single time series of factorableobjects, which is readily outputted interpretable through application ofa succinct nomenclature.

In one embodiment the physician may mark a test result or other datapoint as mistaken or anomalous. In this case the processor splits theanalysis into two—the working analysis (which removes or alters the testresult or other data point) and a background analysis (which maintainsthe original data). The processor may run scenarios in which theoriginal test result stays in effect to determine if conditions occurthat might have been expected from the “so-called” anomalous test. Thebackground will not affect the working analysis but notification may begenerated if a correlation of events is found in a sufficientlysuggestive pattern to warrant a consideration that the original testresults may not have been mistaken and, in fact, would account forconditions that do not fit the current working state (e.g., the statewith the test results removed). Background analyses may be deletedaccording to time (e.g., after a certain amount of time in which nocorrelation to following events is found) or at the request of the useror system operator (e.g., to reduce resource utilization).

In another example the processor may be programmed to generate morefrequent testing binaries to confirm or exclude an apparently evolvingimage. In this way the processor is trying to look as far forward aspossible with additional testing to confirm the motion picture of aparticular failure as early as possible so that the delay associatedwith waiting for the detection of a failure cascade as by varioustraditional threshold breaches is eliminated.

In an example, as part of assuring that the future image is complete,the testing binaries are designated such that the addition of certaindrugs (the alpha event) into the image, may cause automatic orders fortesting to monitor for complications related to the drug (the betaevent) if selected, events, binaries, image components, or MPPC arepresent. In an example, if the physician orders heparin, a testingbinary is generated and added to the image which includes automaticorder for a platelet count every 48 hours. According to one embodiment,the time series objectification processor 336 is objectifying the timeseries of platelet counts to detect a least one fall event (as forexample defined by a negative slope and/or a magnitude of fall and/or athreshold fall), if a declining slope is detected a divergent binary isgenerated and a marker indicating a fall is added to the image along theplatelet count time series, the processor may generate more frequentplatelet testing binaries, to confirm the presence of these divergentbinaries in the image. If multiple divergent binaries are detected thenthe processor may generate different types of testing binaries whereinthe alpha event is the fall in platelet count. This may trigger acascade of testing binaries such as, for example, wherein the alphaevent is a binary that includes a heparin treatment and a fall inplatelet count t and the beta event is, for example, a platelet factor 4assay or/or another assay.

In this way, using the failure imaging processor 360, the delayassociated with waiting for an absolute or relative threshold drop inthe platelet count is reduced. In addition the cascade may includeadditional testing binaries (as for hepatic function tests, to determinethe safety of Argatoban, a medication which may be ordered if the imagecomponents are consistent with heparin induced thrombocytopenia. Herethe advantage of having these binaries and image components as part of aMPPC is evident, because the processor will be examining the images ofthe motion picture for other causes of the fall in platelet count whichmay include cascades indicating TTP as will be discussed and/or occulthemorrhage.

One embodiment programmatically images the parallel physiologictime-series to render a relational pyramid of data with the top of thepyramid representing data at the highest level of analysis andabstraction while data moves down through layers of analysis, the bottomlayer being the raw data streams. The healthcare worker may investigatethe pyramid in the following ways to name a few:

-   -   1) Drilldown—the care worker may navigate into the details of        the data and the rationale of the analysis (i.e., both the        conditions that exist and the rules by which the analysis has        arrived at its conclusion)    -   2) Aspects—viewports into the system which emphasize certain        elements/conditions and de-emphasize (and/or filter out) other        elements/conditions)

These two examples above may be used together allowing the healthcareworker to navigate through the relational pyramid vertically (drilldownthrough levels of analysis) and horizontally (through filters/aspects).

In one embodiment the relational pyramid may be manipulated by thehealthcare worker and/or researcher to consider hypothetical scenariosor scenarios based on the rejection of certain test results or eventswhich may be considered in error, anomalous or otherwise inaccurate.Alternate pyramids may be stored in whole or as differential images.Alternate pyramids may be compared against the working pyramid tounderstand the results of the altered data.

In one embodiment, the processor 304 will automatically consideralternate pyramids under certain conditions—such as the existence ofperturbation for which no precursors may be identified. The suddenexistence of perturbation or of divergence may, by considering the rangeof possible precursors, suggest anomalous conditions: inaccuratediagnosis, faulty monitoring equipment, labeling mistakes, the failureof a patient to take medication as prescribed, to name a few.

According to one aspect, the values and/or patterns of the blood testssuch as the inflammatory mediators is/are compared to the image(s) ofphysiologic perturbation or to the pattern(s) or values of at least onephysiologic parameter, such as the pulse rate, respiration rate, and/orventilation oximetry index to name a few. Upon the detection of anapparent relationship, the processor may automatically order a sufficingnumber of sequential blood tests to confirm that the pattern of theparameter is convergent with the pattern of the blood test therebyproviding strong supporting evidence, reinforcing redundant evidence,that the physiologic parameter and the mediator have a commonphysiologic failure based linkage, such as the failure of sepsis forexample. One embodiment extends that analysis to incorporate specializedinflammatory mediators into the moving picture of failure so thatcomprehensive comparison of the marker or indicator to the image of thephysiologic parameters and treatment is provided.

FIG. 4 shows a UML Static Diagram of one embodiment of the relationalbinary processor 348, which defines relational binaries to therebyorganize the complexity of the electronic medical record for the timelydetection of failure image components. According to this embodiment arelational binary is defined by first detecting an alpha event object,which is defined in terms of its channel (e.g., oximetry) and itscharacteristics (e.g. slope, magnitude, duration.). Then the companion(relational) beta event object is defined, again in terms of itschannel, (e.g., pulse or oximetry) and its characteristics (e.g. slope,magnitude, duration) including the spatial and/or temporal relationshipsto the alpha event. In other words, the beta event may also be aspecified as function of the magnitude, slope, timing, or otherrelationships of the alpha event. Alternatively or in combination thebeta event may be identified as a being between two values each of whichmay be a function of the magnitude of the alpha event.

The actual relationship between the alpha and beta events, whichcomprise the object binaries, is not defined by cause and effect (whichmay not be known with complete certainty) but is rather defined by thepattern relationship such as a temporal, spatial, and/or frequencyrelationship of the events, or simply by their prior designation as arelational pair. For example, the actual relationship between the alphaand beta events comprising a given relational binary could be a causeand effect, two effects resulting from an unmonitored cause, arelationship between two monitoring technologies measuring the samephysiological phenomenon, an expected compensatory response, or apathologic response, to name a few. One object is to identify thepattern relationship of aggregate objects that include a plurality ofrelational binaries so that the actual relationships may be defined.

An alpha event is defined as a perturbation, which is defined in termsof its channel (e.g., oximetry) and its characteristics (e.g., a slope,magnitude, duration, and/or threshold breach). A beta event may bedefined as an expected response event, defined again in terms of itschannel (e.g., pulse or oximetry) and like the alpha event, in terms ofits characteristics (e.g., slope, magnitude, duration). In addition, thebeta event may also be defined by the spatial and/or temporalrelationships to the alpha event or a component or portion of the alphaevent. For example, when the beta event is an expected response event,the beta event may be specified as function of the magnitude, slope,timing, or other relationships of the alpha event. Alternatively, inanother example, the expected response event may be identified as abeing between two values each of which are a function of the magnitudeof the perturbation event. The alpha event and/or the beta may, forexample, be a perturbation event, treatment event, or a diagnosticdesignation event to name a few.

According to one embodiment, there are three basic relational binarytypes; the convergent relational binary, the divergent relational binaryand a null relational binary. (Although others relational binaries maybe provided). A convergent binary is an alpha event combined with anexpected beta event response. If the channel of the expected response ispresent and uncorrupted, but the expected response is not found, then amissing event (comprising, for example, a wave segment or test result ofthe region of the expected response) is specified and the relationalbinary that includes the alpha event and the missing beta event iscalled a divergent binary. If the channel of the expected response isnot present or the wave segment or test result is corrupted in theregion of the expected response then the relational binary that includesthe alpha event and the untested beta event is called a null binary.

An event may be the alpha event of a first relational binary and thebeta event of second relational binary (provided the alpha event andbeta events are each along different parallel channels). With somephysiologic processes, relational binaries cycle or repeat with acertain pattern and this produces a special case of relational binaryclusters or patterns.

In one-embodiment event characteristics may be defined in terms ofmodifiers defined by patient conditional values such as anthropomorphicvalues, age, sex, or preexisting disease, such that the presence ofthese modifiers (as for example provided by a rule system in combinationwith the event definition menu) causes a change in the event definitionparameters and/or threshold values.

In one embodiment the events and/or the relational binaries(convergences and divergences) are aggregated to construct a globalfactorable object to derive a factorable objectified timeline. Thefactorable objectified timeline may be rendered graphically or providedby a nomenclature for example, which identifies the events and the timefrom the onset of the closest preceding event to the onset of thefollowing event.

According to an embodiment, relational binary objects or a specificaggregation or pattern of relational binary objects may bepre-designated by the user to define a failure image component. Theprocessor may then automatically and timely identify the occurrence ofthe failure image component by searching the event streams, divergencebinary streams, and convergent binary streams, which are stored for eachpatient. In the alterative or in combination, all such streams or aportion of specific streams or a grouping of streams filtered forseverity of divergence (for example) may be aggregated and rendered forperiodic viewing wherein, for example, the temporal relationships of forexample divergent binaries or of the occurring failure image componentsare easily recognized or specifically indicated.

FIG. 4 shows a convergence analysis static Model according to oneembodiment including UML Static Diagram of the classes (andrelationships) which the Relational binary processor uses during theprocessing, analyzing and synthesizing of, in this case, electronicmedical record input streams. Objects created from these classesrepresent the identified perturbations as well as attemptedidentifications, which failed due to the absence of data streams.User-interfaces, reporting systems, business intelligence and datawarehousing sub-systems, notification mechanisms, alarms and other humanor software application interfaces access this analysis structure toaggregate, further analyze, store and/or react to the results ofanalysis.

In the depicted embodiment, the case 404, channel 408 and time series412 classes represent the data streams from which the analysis may bederived. These classes may be defined as disclosed in U.S. Pat. Nos.6,609,016 and 7,081,095 and U.S. patent application Ser. Nos. 11/431686,11/351449, and 11/148325 the specifications of each of which areincorporated by reference for all purposes in their entirety. For eachcase 404, one or more case analyses 416 may be constructed. A caseanalysis 416 is the result of a case 404 being submitted to therelational binary processor 348 with a specified binary definition set.A single case 404 may be analyzed with multiple binary definition setsresulting in one case analysis 416 per binary definition set applied.

A case analysis 416 is primarily composed of the relational binariesidentified during processing. In one embodiment, the relational binariesare one of three types—convergent binary 440, divergent binary 456 andnull binary 428. The case analysis 416 contains a collection of each ofthese pairs and may have zero of more pairs in each of thosecollections. As discussed, relational binaries are composed ofrelational events 444. The structure of the relational binary (i.e., thetype of events which compose the relational binary) is defined by itstype and the classification is provided to fix the structure of theserelationships. In one embodiment, all relational binaries contain analpha event, which is a true event (e.g., represents the identificationof a pattern or a threshold violation, see FIG. 5). In an embodiment,the type of beta event identified makes the distinction between objectbinary types. For example, a convergent binary 440 represent arelational pair of events wherein the beta event has an expectedrelationship to the alpha event as described in the binary definitionset. A relational binary may have either a true event 444 or a missingevent 460 as a beta depending on what has been specified as the expectedcondition. If a true event 444 was specified in the relational binarydefinition then the associated convergent binary 440 may have a trueevent 444 as a beta event. If a missing event 460 was specified then theassociated convergent binary 440 may have a missing event 460 as a betaevent. The class structure therefore allows for zero or one event 444and zero or one missing event 460. In a presently preferred embodiment aconvergent binary 440 may not contain two beta events.

Divergent binaries 456 represent a relational pair of events identifiedin a relationship that contradicts the expected relationship asdescribed in the binary definition set. Therefore a divergent binary 456may have either a true event 444 or a missing event 460 as a betadepending on what has been specified as the expected condition. If atrue event 444 was specified in the binary definition then theassociated divergent binary 456 may have a missing event 460 as a betaevent. If a missing event 460 was specified then the associateddivergent binary 456 may have a true event 444 as a beta event. Theclass structure therefore allows for zero or one event 444 and zero orone missing event 460. According to one embodiment, a divergent binary456 may not contain two beta events.

Null binaries 428 represent the existence of a condition in which analpha event was identified but the data stream from which the expectedbeta event is to be derived is unavailable to the relational binaryprocessor 348. Events 444 may be isolated (e.g., not part of anyidentified relational pair) or part of one or more binary. The channelevent stream 424 provides an aggregation of events 444 ordered by timeand separated by channel 408. A true event 444 is a wave segment 448(e.g. inherits wave segment) while a missing event 460 is associatedwith a wave segment 448 that represents the section of the channel 408that was searched for the event described as expected in the binarydefinition set. Null events 432 are not associated with wave segments448 because the channel 408 to which they would have been attached orthe relevant section of that channel 408 is unavailable or corrupted.The relational binary processor 348 will convert null binaries 428 toconvergent 440 or divergent 456 binaries as channels 408 of data becomeavailable.

The analysis contains aggregations of binaries, which repeat (e.g.,cycling reciprocations) in three aggregation classes: repeatingconvergence 436, repeating divergence 452 and repeating null 420. Tofurther clarify this structure it may be useful to describe the order ofoperation within an exemplary embodiment of the relational binaryprocessor as it constructs the analysis according to one embodiment.

-   -   1. Each channel 408 in turn is iterated through and named events        492 and threshold violations 484 (Events which may be identified        without reference to relational pairs) are identified and placed        into channel event streams 424    -   2. The channel streams 424 are iterated through to match any        identified events 444 with candidate alpha events (as defined in        the specified binary definition set). A single event 444 may        match any number of alpha event definitions and each one is        considered a candidate alpha event.    -   a. For each candidate alpha event, the specified search region        is examined for the expected beta event    -   i. If the channel 408 in which the expected beta event is        unavailable or corrupted    -   1. A null binary 428 is created (along with its associated null        event 432)    -   2. The conditions are examined to determine whether a Repeating        Null 420 should be created or appended to    -   ii. If the expected condition is found    -   1. A convergent binary 440 is created    -   2. If a relational event 488 was identified in the process it is        created and added to the channel event stream 424    -   3. The conditions are examined to determine whether a repeating        convergence 436 should be created or appended to    -   iii. If the expected condition is not found    -   1. A divergent binary 456 is created    -   2. If a relational event 488 was identified in the process it is        created and added to the channel event stream 424    -   3. The conditions are examined to determine whether a repeating        divergence 452 should be created or appended to    -   3. Failure image components and aggregate failure image        components are identified (See Below)

FIG. 5 shows an event type static model. According to an embodiment,events may be represented as one of three types: threshold violation484, named event 492, and relational event 488. Threshold violations 484represent the existence of a breach of some specified, calculated orderived limit within an associated channel 408. Named events 492 andrelational events 488 represent an identified unipolar pattern within achannel 408. Named events 492 differ from relational events 488 in thatthe parameters with which the pattern is identified is not a function ofelements of an associated event (e.g. a limiting event 496). Limitingevents 496 within the context of a convergent binary 440 are the alphaevent of the related relational binary.

A limiting event 496 may be either a threshold violation 484 or a namedevent 492, but, in one embodiment, not a relational event 488. In anembodiment limiting events 496 may be relational events 488 and therelational binary processor employs recursive algorithms to determine acomprehensive set of events. Threshold violations 484 and named events492 may be isolated events (e.g. identified independent of a relationalbinary). Alpha events of a relational binary may be either a thresholdviolation 484 or a named event 492 but, in this embodiment, not arelational event 488. In an embodiment this rule is relaxed to providethe ability to produce a relational cascade. In an embodiment, alphaevents may be relational events and the relational binary processor mayemploy recursive algorithms to determine a comprehensive set of events.

FIG. 6 shows an aggregate failure image component static model, whichprovides further clarification to the presently preferred embodiment ofthe patient safety processor. The aggregate failure image is one typeimage which will be searched for. After the relational binaries areidentified, the relational binary processor 348 may aggregate theseidentified pairs into aggregate failure image component objects, whichrepresent the identification of patterns of events and binaries. Theaggregate failure image components 524 are created with respect to afailure image component definition set. A failure image componentdefinition set is associated with a single binary definition set, butmultiple failure image component definition sets may be created for abinary definition set.

An aggregate failure image component 524 has two collections of failureimage component elements 528. The first is a set of failure imagecomponent elements 528 that was identified in a specific sequence. Thesecond represents failure image component elements 528 that simply fellwithin the specified search window (e.g., existence, not sequence issufficient for aggregation). Aggregatable 532 is one embodiment of alightweight interface, which allows the analysis objects (536, 540, 544,548, 552, 556, 560, 564, 568) to participate in the aggregation. Theanalysis objects include convergent binaries 536, divergent binaries540, null binaries 544, repeating nulls 548, repeating convergences 552,repeating divergences 556, events 560, missing events 564 and nullevents 568 may all participate in an aggregate failure image component528.

FIG. 7 shows a binary definition set static model. The binary definitionset model represents the objects that are part of the binary definitionsets used by the relational binary processor 348 to create theconvergence analysis. A binary definition 590 represents the parametersused to identify a relational binary. A binary definition 590 is made upof four key elements—the binary type 606, the search window definition618 and the definitions of the alpha 630 and expected beta events 594.

FIG. 8 shows an embodiment of a convergence editor, which provides theability for the creation, and modification of a binary definition set,which may be used by the relational binary processor to create theconvergence analysis. A binary definition set may be represented as aconvergence model—a visual representation of the object instances shownin FIG. 7. The user interface includes a design surface 764 and anelement toolbox 700, which allows for the drag-and-drop creation andmanipulation of a subset of the convergence model called a binarydiagram. The aggregation of all binary diagrams created with a singlename constitutes the entire convergence model and may be persisted as abinary definition set in the relational database or in an XML file toname a few. Breaking a convergence model into binary diagrams allows formultiple views into the model. These views are not mutually exclusive(i.e., the same binary definition may be represented in multiplediagrams) and therefore provide views into model at various levels ofcomplexity and points of reference.

The box on the left is the convergence element toolbox 700 whichpresents the visual elements which may be added to the design surfaceand therefore to the binary diagram. The shapes represent events thatmay be added. The three event types available correspond with the eventdefinition classes in FIG. 7: named event 598, relational event 602 andthreshold violation 622. The relationships 768 section of the toolbox700 presents a set of lines, which may be used to connect two events tocreate a relational binary. The line chosen determines the binary types606, binary types include: expected 716, analogous binary 720, possiblecycling 724, verify non-existence 728, reoccurring verification 732. Thevisual icon attached to the line may cue the user to its type. Thebinary type 606 determines the type and frequency of search that mayoccur when the candidate alpha event is identified. For example, thereoccurring verification type 732 may generate multiple binaries for asingle candidate alpha event because it directs the relational binaryprocessor to search for the expected event with a specified frequency,generating binaries at each interval. Some binary types may be used incombination (e.g., reoccurring verification 732 and verify non-existence724). Each relationship added to the design surface 764 must have atleast one time interval provided (e.g., 768) which represents the searchwindow definition 618 for the binary definition 590. Each relationshipmay be directional. The line includes an arrow end-style on the end thatrepresents the beta definition 626. The end without an arrow representsthe alpha definition 630.

Each pair of events, which has a connecting relationship, represents asingle binary definition 590. In the above figure, the following sevenbinaries:

-   -   1. An analogous binary between nasal pressure fall and oxygen        fall (736, 772, 740)    -   2. A possible cycling binary between oxygen fall and oxygen rise        (740, 773, 748)    -   3. An expected relationship between oxygen floor breech        threshold violation and oxygen rise (744, 768, 748)    -   4. An expected relationship between oxygen rise and oxygen fall        (748, 770, 740)    -   5. An analogous binary between oxygen rise and nasal pressure        rise (748, 774, 752)    -   6. A verify non-existence binary between oxygen fall and pulse        rise (740, 771, 756)    -   7. A verify non-existence binary between oxygen fall and pulse        fall (740, 769, 760)

This diagram does not represent all of the relationships of each ofthese events. It is an example of a subset view into the overallconvergence model with a focus on sleep apnea. Relationships andelements may be removed from this diagram without removing them from theentire model (i.e., the editor distinguishes between “Remove” whichremoves the element from the diagram but not the model and “Delete”which removes the element from the diagram and the model [including allother diagrams]). A diagram may be constructed that shows all of theevents and relationships, but it would likely be so large and complex asto be unreadable.

The editor will check the diagram for validity before persistence or atthe user's request. For example, a relationship without a beta eventwould invalidate a diagram. An invalid diagram may invalidate theconvergence model. It is preferred that a convergence model cannot bepersisted into a binary definition set. The editor allows for an invalidstate to provide flexibility during diagram construction. Further, ifthe target binary definition set is associated with failure imagecomponent definition sets that are available to the editor, the editormay warn of conflicts with associated models by changes to the diagram.Depending on editor settings, these changes are disallowed, or thechanges may be propagated into the failure image component.

Each diagram element may be manipulated in a more detailed way throughproperty editors associated with the element type. The property editorsprovide access to all editable properties of the associated definitionobjects such that the editor is sufficient to construct a completebinary definition set. The editor provides for adding text, notes, linesand other visual elements to the diagram to increase human readabilityand to communicate between users. These additional visual elements haveno affect on the binary definition set.

This structure may be understood within the context of the userinterface modeled in FIG. 7 that may be used to visually construct thebinary definition set. Specifically, FIG. 7 depicts a binary diagramwithin the convergence editor which pertains to the monitoring of sleepapnea. Each pair of events (e.g., 744, 748), which has a connectingrelationship (e.g., 754), represents a single binary definition 590. Theconnecting line between the two events represents the binary type 606.Binary types may include: expected 716, analogous binary 720, possiblecycling 724, verify non-existence 728, and reoccurring verification 732.The binary type 606 determines the type and frequency of search that mayoccur when the candidate alpha event is identified. For example, thereoccurring verification 732 type may generate multiple relationalbinaries for a single candidate alpha event because it directs therelational binary processor to search for the expected event with aspecified frequency, generating relational binaries at each interval. Inan embodiment, some binary types may be used in combination (e.g.,reoccurring verification 732 and verify non-existence 728). The boxcontaining a pair of time offsets 768 represents the search windowdefinition 618. This definition contains the start and end time offsetsfrom the end point of the alpha event for which the beta event should besearched in the target beta channel. Finally the shapes represent thealpha and beta event definitions. These definitions provide theparameters with which the relational binary processor may search theidentified wave segment for the existence of a unipolar pattern (i.e.,meeting the criteria defined by named event definition 598 or relationalevent definition 602 or threshold violation (i.e., meeting the criteriadefined by threshold violation definition 622).

FIG. 9 shows a failure image component definition static model. Thefailure image component model represents the classes that are part ofthe failure image component definition sets used by the failure imageprocessor to identify and create aggregate failure image components. Afailure image component definition represents the set of elementdefinitions and their relationships, which allow the failure imageprocessor to determine whether the pattern of elements meets thecriteria of the specified failure image component. FIG. 10 shows anembodiment of the aggregate failure image component editor that providesthe ability for the creation and modification of a failure imagecomponent definition set, which will be used by the failure imageprocessor, in coordination with a binary definition set, to create aconvergence analysis. A failure image component definition set may berepresented as a failure image component—a visual representation of theobject instances shown in FIG. 9. The user interface includes a designsurface 832 and an element toolbox 780, which allows for thedrag-and-drop creation and manipulation of a subset of the failure imagecomponent called a failure image component diagram. The aggregation ofall failure image component diagrams created with a single nameconstitutes the entire failure image component and may be persisted as afailure image component definition set in the relational database or inan XML file to name a few. As with the convergence model, failurediagrams are views into the model that provide visualizations at variouslevels of complexity and points of reference.

A failure image component definition set is associated with, anddependent upon, a specified binary definition set. A failure imagecomponent definition set, and therefore a failure image component andall its corresponding diagrams, cannot be created without thespecification of a binary definition set. Further the specified binarydefinition set provides and limits the events and binaries that may beused to create the failure image component diagrams.

This structure may be understood within the context of the userinterface in FIG. 10. Each diagram represents a single failure imagecomponent definition 650. In this embodiment, a failure image componentelement definition 662 may either be a binary definition 674 or an eventdefinition 678 (but in one embodiment, may not be both). These failureimage component element definitions 662 represent the existence of aspecific event or relational binary. If a specific sequence of elementsis defined to identify the failure image component then the sequence isspecified with connectors and time offsets (e.g., 812, 824, 816, 828,and820). Each shape container (a shape that contains other shapes)represents a failure image component element definition 662. A failureimage component element definition 662 includes both a binary definition674 and a binary mode. The binary mode 666 indicates the type of binarythat must be created by the binary definition 674 within the analysis(e.g., convergence, divergence or null). Within FIG. 10, the mode isspecified by selecting the binary container (e.g., 784, 788, and 792)from the toolbox 780. An isolated shape without internal shapesrepresents an event failure image component element 678. An eventfailure image component element 678 includes both an event definition678 and an event mode 670. The event mode 670 indicates the type ofevent that must be created by the event definition 678 within theanalysis (e.g., event, missing event or null event).

Failure image component element toolbox 780 in FIG. 10 presents thevisual elements that may be added to the design surface 832 andtherefore to the failure image component diagram. The large bold-linedcontainer shapes (784, 788, and 792) represent failure image componentelements that refer to a binary while the smaller shapes (796, 800, 804)represent failure image component elements that refer to events(isolated or part of a binary). The three binary element types availablecorrespond with the available binary modes 666: Convergence, divergenceand null. Each binary dropped on the surface may subsequently lead tothe selection of a binary definition 674 from the associated binarydefinition set. The design surface is split into two sections—sequencedand non-sequenced. Elements in the sequenced area correspond to thesequenced mode aggregation 654 in FIG. 9. These elements involve arelationship in time and therefore a relationship may be specifiedbetween them (e.g., 824). The relationships section 836 of the toolboxpresents a set of lines, which may be used to connect two failure imagecomponent elements (either binaries or events) as part of the overallaggregate. Each relationship added to the design surface must have atime interval provided (e.g., 828) which represents the search windowdefinition associated within the sequenced mode aggregation 654. Eachrelationship is directional indicating precedence in the sequence 654.

Zero or more sequences may be specified, but if an element is placed inthe sequenced section it is defined as part of a sequence. Elementsplaced in the non-sequenced section cannot have relationships. Onlyexistence is specified within the overall time-frame specified for thefailure image component. The failure image component diagram differsfrom the binary diagram in that the diagram itself represents anentity—the failure image component definition 650—and is not simply acollection of other entities (e.g., binaries in the case of the binaryeditor). Removing elements changes the definition of when a failureimage component will be identified. All elements added to the failureimage component diagram represent an “and” relationship foridentification purposes (i.e., all elements and sequences must exist forthe failure image component to be identified). In one embodiment, tocreate “Or” scenarios, multiple failure image component diagrams arecreated with variation representing the “or” combinations. The editormay check the diagram for validity before persistence or at the user'srequest. The editor allows for an invalid state to provide flexibilityduring diagram construction.

Each diagram element may be manipulated in a more detailed way throughproperty editors associated with the element type. The property editorsprovide access to all editable properties of the associated definitionobjects such that the editor is sufficient to construct a completefailure image component definition set. The editor provides for addingtext, notes, lines and other visual elements to the diagram to increasehuman readability and to communicate between users. These additionalvisual elopements have no affect on the failure image componentdefinition set.

FIG. 11 provides an example of a binary diagram referring to heparintherapy in which the following binary definitions are specified:

-   -   1. A reoccurring verification binary 854 between heparin therapy        850 and ptt rise to therapeutic range 858.    -   2. A verify non-existence binary 866 between heparin therapy 850        and pulse rise 862.    -   3. A verify non-existence binary 882 between heparin therapy        850and blood pressure fall 870.    -   4. A verify non-existence binary 886 between heparin therapy 850        and hemoglobin fall 874.    -   5. A verify non-existence binary 890 between heparin therapy 850        and platelet count fall 878.

FIG. 12 provides an additional example of a binary diagram referring toinsulin therapy in which the following binary definitions are specified:

-   -   1. An expected binary 922 between insulin therapy 920 and blood        sugar fall 924 to therapeutic range.    -   2. A verify non-existence binary 926 between insulin therapy 920        and blood sugar breech 930.    -   3. A verify non-existence binary 926 between insulin therapy 920        and confusion 928.

FIG. 13 provides an additional example of a binary diagram referring tonarcotic therapy in which the following binary definitions arespecified:

-   -   1. A reoccurring verification binary 944 between narcotic        therapy 940 and pain score fall to therapeutic range (948)    -   2. A verify non-existence binary 952 between narcotic therapy        940 and oxygen fall 956.    -   3. A verify non-existence binary 960 between narcotic therapy        940 and blood pressure fall 961.    -   4. A verify non-existence binary 962 between narcotic therapy        940 and respiratory rate fall 964.    -   5. A verify non-existence binary 966 between narcotic therapy        940 and confusion 967.

FIG. 14 provides an additional example of the failure image componenteditor in which three non-sequenced binaries (970, 971, 972) are definedas sufficient to identify possible heparin-induced hemorrhage.

FIG. 15A shows a failure image frame 973 of a patient's physiologicsystem and care and demonstrates one exemplary image according to oneembodiment as generated by the failure image processor. The image shownis indicative of dynamic progression from an image suggestive ofstability to an image suggestive of a failure cascade of septic shock.This is the one of image for which the patient safety processor isdeployed as a “search engine for pathophysiologic cascades” maycontinuously search when deployed into use with a patient. The imagedisplays objectified events that met criteria as up and down arrowsindicating whether they are rise events or fall events respectively.Minor time series variations (such as detected minor rises or fallstypical of signal noise, which fail to meet criteria by theobjectification processor as events) are represented on each time-lineas open circles along parallel time lines. (The visualization of suchvariations may be turned on or off as desired). The detected events arecombined with other events to form binaries which are then combined toproduce an image of relational patterns that include aggregate binariesand individual events defining the dynamic state of the patient'sphysiological system and of the medical care applied to the physiologicsystem during the time interval of each respective image. Within thecomplete image, smaller failure images aggregate to produce the largerimage of aggregate failure (in this case, of septic shock). In real timethis is a motion picture image which may be shown with this rendering orwith an alternative rendering, such as an actual digital motion pictureof the patient within these parameters reanimated in the MPPC.

Since FIG. 15A is a late “time lapsed” frame of a MPPC that hasexhibited many earlier frames, the patient safety processor outputprovided that confidence that the cascade image detected search engineis septic shock was high. Representations of rise events or fall eventsare depicted as up-arrowheads and down-arrowheads respectively on eachtime line 974. The timelines 974 are grouped into categories 975. Thefirst event detected within the time interval of the image is aperturbation event—a rise event of the neutrophil count 976 shown by theupward pointing arrowhead on the neutrophil timeline. This perturbationevent is combined by the relational processor to a second perturbationevent—a rise in respiratory rate 977 also shown by an upward arrowhead,to generate the first relational binary 978 (combined in the figure bythe arrow connecting 976 and 977). Each subsequent perturbation in theimage is designated by its timeline and arrowhead. An arrowhead with acircle around it designates perturbations determined by testingautomatically ordered by the patient safety processor in response to thedetection of a particular image. In an example the rise event ininflammatory mediators or indicators 979 was ordered by the patientsafety processor to better define the inflammation portion of the imagewhich was somewhat obscured because the early images demonstrated a risein neutrophil count, a rise in pulse, and a rise in respiration rate butwith a normal temperature. Since this ambiguous image must be betterdefined to decide care, testing for inflammatory mediators/indicators isautomatically ordered by the processor to better complete the image.

Using these basic designations the image of FIG. 15A reveals a clearimage frame (a time lapsed snap shot) detected by searching of an MPPCthat includes perturbations of inflammation, followed by a hemodynamicperturbations, followed closely by respiratory perturbations, and thenrenal perturbations in an expanding and linked cascade 980. The initialrise in Neutrophil count 976, the first detected perturbation event,will have completely disappeared later in the cascade such that frameslate in a failure process are best viewed with the sufficient scale toobserve the onset of the cascade 980. The image shows a complete lack ofany events along the temperature timeline 981. In the absence of theanalysis provided by the processor 304, the lack of a fever may misleada healthcare worker, who may think of fever as a reliable indicator forthe early detection of sepsis. However, the processor 304 is programmedto recognize that it has rendered or found an incomplete image and thenseeks to complete the image by ordering testing for inflammatorymediator 979. This testing serves as a “surrogate image components” fora rise in temperature thereby establishing that the entire failure imagedoes in fact exhibit an early component of inflammation.

Two drug treatments are evident in the image, the antibiotics vancomycin982, designated by its dose on the time line, and levofloxacin 983,similarly designated. Also a rise in IV fluids in the form of normalsaline 984 is indicated. All of these treatments come late after theimage has long been indicative of a high probability of sepsis. (Thisdelay, which may be detected in real-time by the patient safetyprocessor, suggests poor and ineffective care, which has ignored orotherwise been poorly responsive to the patient safety processor. Theprocessor may be programmed to provide an indication of the quality ofthe care provided. Time lines, which include the care worker or ward maybe provided so that delays may be linked to particular locations or careworkers).

The image of the progressive cascade 980 shows the drug treatmentscomponents 982, 983 of the image are too late because they appear withinthe image very late along the cascade 980. The late portions of theimage of the cascade 980 also include a very ominous beta comprising arise in anion gap 985. The addition of this new image component providesa mature image of cascade 980, which is now strongly indicative of ahighly fatal stage of septic shock. Other image views may be forexample; specific expanded portions of the time lines, specific expandedviews of aggregate failure components along the timeline portions,specific groupings of the timelines, overviews of perturbationprogression from group to group (an example of this is shown in FIG.19), to name a few.

FIG. 15B is the failure image frame of FIG. 15A with portions of theimage being separated into sequential states of inflammation 986,systemic inflammatory response syndrome 987, presumptive severe sepsis988, presumptive severe septic shock 989.

FIG. 15C is an early failure image frame from real time imaging of theprocess in FIG. 15A that demonstrates that there may be little in thesefirst perturbations to warn of the impending cascade towards sepsis. Thefirst “spark”, a rise in neutrophil count 990, evident in this image isentirely non-specific despite the fact that it, in retrospect, heraldsthe onset of septic shock, completely disappears by the time this motionpicture has reached the point illustrated in FIG. 15D (see below) inwhich focused testing, more frequent CBC testing, and/or more frequentvital sign measurement to determine the significance of this rise inNeutrophil count may be suggested or ordered by the processor 304 toexpand the image to more quickly move toward a more specific image.

FIG. 15D is a failure image frame from real time imaging of the processin FIG. 15A. This frame demonstrates early image components ofinflammatory, hemodynamic, and respiratory augmentation 991 combinedwith early immune failure 992. As indicated by the image, serious sepsisis highly likely if treatment does not occur by the time this frame ofthe MPPC has passed.

FIG. 15E is a failure image frame from real time imaging of the processin FIG. 15A. This frame demonstrates demonstrate the image components ofinflammatory, hemodynamic, and respiratory augmentation 991, with immunefailure 992, but now with image components indicative of a decline inrespiratory gas exchange 993 and fall in platelet count 994. Asindicated by the image, serious sepsis is even more likely than or thestage shown in FIG. 15D if treatment does not occur by the time theseframes of the MPPC have passed.

FIG. 15F is a failure image frame of FIG. 15A to demonstrate that theimage now shows expansion the failure cascade from the frame in FIG. 15Eto now include the image components of metabolic failure 995, renalfailure 996, hemodynamic failure 997 and respiratory failure 998. Thisis the point wherein medical intervention for sepsis begins in manypatients monitored by today's electronic medical record and monitoringsystems. The introduction of treatment at this point of the movie isoften entirely ineffective. The introduction of fluid resuscitation 999at this late frame of the image will likely have little effect onprogression of the patient.

FIG. 16 shows a time lapsed failure image frame of the failure cascadeof congestive heart failure. Note the first perturbation event detectedby the processor is hemodynamic (a rise event in pulse rate 100), ratherthan inflammatory as in FIG. 15A. The next detected perturbation eventis respiratory, a rise in respiratory rate 102 which combined with therise in pulse 100 produces the first relational binary 104. There is afall in the ventilation indexed oximetry value 106 producing a secondrelational binary 108 with the rise in respiratory rate 102. The rise inrespiration rate 102 is the beta event of the first relational binary104 and the alpha event of the second relational binary 108. Togetherthese two joined relational binaries form an image component 110, whichmay be followed back to the initial onset of the image of the nascentcascade 112. Treatments including furosemide 114 and metoprolol 116 areinitiated fairly close to the onset of the image of the nascent cascade112 but are not effective in preventing subsequent occurrence of animage of a progressive cascade 118. This image of a progressive cascade118 is defined by the both the components and length of the MPPC. Theprocessor 304 upon detection of this failure image may search for thefundamental cause of the cascade progression by automatically orderingcardiac enzyme (not shown) and other tests if the safety committee ofthe hospital desires these types of proactive measures. Note the cascade118 includes the development of atrial fibrillation 120 and subsequentfurther deterioration.

FIG. 17 shows a failure image frame of sleep apnea. The firstperturbation events occur in a group that includes cycling events ofpulse rate 122, respiratory rate 124, SPO₂ 126, and pulse upstroke 128.These occur after the initiation of a narcotic dose of 3 mg IV 130. Theaggregated image components showing cycling 132 then repeats to producesecond such image components g 133 and third such image components 134.The SPO₂ cycle 135 portion of the third image components showing cycling134 becomes more severe with recovery failure 136. CPAP treatment 137 isgiven timely and no further narcotic is given. In this case, there is noimage of an expanding cascade or progressively declining respiratoryrate or declining SPO₂ to indicate life-threatening narcotic inducedsustained hypoventilation. On later review, as in morning report or withteaching rounds, the entire MPPC, which contain this frame, may bereviewed by moving along a fast framed image to better visualize thesubtleties of the progression furthermore the physician or nursing groupmay drill down to see that actual time series (as, for example, by rightclicking on the SPO₂ cycling symbol 137). The decision as to whether ornot the treatment in this case rendered timely care may be assessed. Inan example, the physicians in the session may petition the patientsafety committee to adjust the patient safety processor to provide arecommendation for earlier automatic RT department notification, alongwith the nurse notification when images such as those defined in theearly portion of this motion picture are present. In this way thePatient Safety Processor becomes an integral part of the continuousquality improvement actions of the hospital system with the goal beingto move treatment and testing leftward into the earliest frame whichprovides sufficient image support for the treatment or testing. The goalis to a continuing move toward earlier treatment of the source of theearly perturbations before the cascade develops. According to oneaspect, the processor 304 is integrated into the continuous qualityimprovement process and the processor 304 becomes an integral part of ahospital's quality improvement committee meetings and a major source ofhospital-wide as well as focused analysis and a mechanism to rapidlyinstitutionalize quality improvement focused change.

FIG. 18 shows a failure image frame indicative of a high confidence ofthrombotic thrombocytopenic purpura (TTP) a rare thrombotic andinflammatory condition which mimics the image of septic shock. TTP maybe caused by the inhibition of ADAMTS enzyme by autoantibodies but thisdisease may also be rarely triggered by the very common drugclopidogrel. TTP often occurs within 2 weeks of drug initiation and mayresult in serious adverse events if not detected. Unfortunately, TTPshares many of systemic response features of the very common disorder ofsepsis (FIG. 15A) which also causes thrombocytopenia. Since sepsis is amuch more common condition, misdiagnosis of sepsis in the presence ofTTP is a high possibility; furthermore, as with most pathophysiologicfailures, both processes may coexist in a single patient along withother related conditions such as systemic lupus erythematosis andpancreatitis. Despite the fact that the moving images of failure in TTPand sepsis are similar, misdiagnosis of sepsis in the presence of TTPmay be serious, since TTP cannot be expected to respond to antibiotictreatment and misdiagnosis of TTP s in the presence of sepsis may alsobe serious, since sepsis cannot be expected to respond to plasmaphoresiswithout antibiotics.

TTP is associated with the accumulation of large multimers of VonWillebrand factor, which damages red blood cells and induces extensivemicro vessel thrombosis, resulting in confusion, renal failure andmicroangiopathic anemia, which is associated with sentinel schizocytesthat may be detected in the peripheral smear of blood (if the diagnosisis suspected and the test is ordered). Thrombocytopenia, renal failure,and hematuria may appear earlier in this process than with sepsis butthese early findings are only an image clue and do not differentiate twoMPPCs.

The MPPC suggestive of TTP is generated by the processor 304, with theprocessor 304 indicating a failure image consistent with the possibilityof sepsis and/or TTP and other less likely conditions such as an acutevasculidity. The processor 304 may output non-specific characterizationsof the image such as “image consistent with a life threatening acute orsub-acute thrombotic and inflammatory augmentation” and may present adifferential diagnosis of the processes that may generate such an image.

Also, as for example upon the detection of a threshold frame or frames,the processor 304 may automatically order the peripheral smear, bloodcultures, urine cultures, sputum cultures, chest x-ray, ANA, pancreaticenzymes, renal sediment, and ANCA study to enlarge and fill in the gapsof the image as rapidly as possible. It is the hospital experts who willultimately decide the cost-effective balance of ordering these tests asdefined by the position the tests are ordered along the cascade. Ifdesired, the reports form the chest x-ray may include a section thatwill appear as a time series (as for example, a step function). Theradiologist in the interpretation may enter an indication of pulmonaryinfiltrate, pulmonary edema, and the like and may indicate worse orbetter which may result in a step change from the last test. In thismanner, the results of studies such as chest x-rays become a source fortime series rendering and incorporation into the failure imagingprocess.

The presence of an image that includes image components defining afailure cascade 139 that includes inflammatory—hemodynamicrespiratory—augmentation (IHRA) 140 with an early fall in platelet count142, a fall in the ventilation oximetry index (VIO) 144, a fall orthreshold value of hemoglobin 146, an rise or threshold value of aconfusion score 148, and/or a rise or threshold value of red blood cellsin the urine 150, and/or a rise or threshold value of creatinine 152.Together the combination of image components produces a MPPC suggestiveof the possibility of TTP and/or sepsis and/or other less commonprocesses. For example, if the patient had just received blood it wouldsuggest a possible transfusion reaction.

The processor 304 may indicate to the healthcare worker the gravity ofthe image, a differential diagnosis as suggested by the image, thegeneral type and/or physiologic description of failure cascade present,as well as a notification that the detection by the patient safetyprocessor of this type of image may lead to prompt notification of theattending physician and transfer to ICU. If the image has insufficientbinaries because results are not available to define enough betacomponents to define the presence of the failure image suggestive of TTPwith a sufficient confidence level to take action, the unavailable testsare ordered upon the detection of the partial image in an attempt tocomplete the image. Note in FIG. 18, the detection of the imagecomponents suggestive of the possible presence of a complete MPPC of TTPtriggered the test for schizocytes 152 in an attempt to complete the TTPimage. The detection of a threshold value step function, and/or rise inschizocytes combined with the rest of the image triggers the warning ofthe potential presence of TTP. FIG. 18 reflects suboptimal care in aretrospective case that was detected by the processor because theplasmaphoresis 154 order was carried out too late. Such delays may bedetected in reviews of medical history data, and the processor may beconfigured to provide an automatic report of variance to the qualityimprovement department of the hospital.

In certain embodiments, human delay in physically following the ordersof the patient safety processor may be addressed by building escalatingalarms into the processor 304. The time in carrying out the order isdetermined by the processor 304, and the processor 304 may be programmedto up-indicate the warning upon increasing delay. To prevent this delay,the processor 304 may be programmed notify another station if action isnot taken in response to detection of various evolving failure imagessuch as the one in FIG. 18. These may be decided, for example, by thehospital quality improvement committees or by individual physicians ornurses if desired so that the patient safety processor improves overtime and may be adjusted to compensate for the diligence of thehealthcare worker. The patient may receive Levofloxacin early to coverthe possibility of sepsis as the image was also consistent with sepsisand the healthcare workers decided to empirically treat for sepsis(albeit with somewhat limited antibiotic coverage). However, the cascadeproceeds despite antibiotic therapy. Since a cascade is an imagecomponent and the relationship of the cascade, its growth, and itsfeatures and its timing within an MPPC in relation to the dose, timing,and type of treatment also forms part of the MPPC, these relationshipsmay be automatically assessed by the processor in real-time to determineif treatment is effective. The hospital safety committee or infectiousdisease committee may decide whether or not to reprogram the patientsafety processor to make antibiotic suggestions based on various rangesof failure images before the results of cultures are known.

FIG. 19 shows an overview image of perturbation onset and progression asderived from the time lapsed MPPC of FIG. 15A wherein the perturbationsin each grouping are incorporated into an aggregate index along a singlesmoothed time series for each group. Note this is a typical progressionof sepsis with initial involvement of the inflammatory group 160 theneach other group is involved in progression. Note the late timing of thetreatment 162 is particularly evident in this summary view derived formthe more complex images.

Rather than, or in combination with, an index, if desired the processor304 may be programmed to provide an indication of the severity andnumber of the aggregate perturbations in each group. These may be forexample designated by many enlarging or colored arrows, other icons,and/or timed instability scores, to name a few. Many such options may beincluded so that the user may define his or her preference to visualizethe sequence and patterns of cascade progression across groups.

A range of expert and pattern recognition systems may be applied toanalyze the images and the image components generated by the failureimage processor. These comprise the image identification processor. Inone embodiment the image identification processor works with the failureimage editor, which allows the user to select the images for detectionusing for example from a drag and drop interface. In an embodiment thedrag and drop interface provides for the discretionary selection of, forexample, the time-series type to be selected, then the events andbinaries are selected on each time-series type in order and the rangesof relative positions and orders of the events and binaries is selected.In this example, the failure image editor allows customization of thedesired ranges for the components of the images (and therefore theranges of the images themselves) to be selected as well as the responseof the image identification processor to the detection of a given imageand/or images. The failure image editor may allow for selecting theranges of timing and order of the events and binaries to generate aspecific output such as a proposed diagnosis, warning, order for moretesting or imitation or termination of treatment. The imageidentification processor may also be adaptive such that a physicianinputs the diagnosis present, such as for example septic shock, with agiven image. The physician may also capture a given image or set ofimages into the failure image editor to then select ranges about theevents and binaries within the image which also would have indicated thepresence of septic shock so that the adaptive image processor may learnmore quickly.

FIGS. 15A-F, 16, 17, 18 and 20 represent a 2 dimensional “time lapsed”snapshot view four MPPC after they have proceeded to advance states.This view also provides an alternate user interface for the creation andediting of the Failure Image Definition Set. Researchers may use afailure image editor to create and manipulate failure models.

In one embodiment researchers work from the top down to define failureimages. Researchers begin by selecting a set of channels in which theywant to “paint” the failure image. FIG. 20 depicts the failure imageeditor being used to “paint” the narcotic-induced ventilationinstability failure image. Channels (100, 102, 104, 106, 108) may beordered in any number of ways, by sorting, categorizing or by simpledrag-and-drop selection of location within the failure image editor.Channels may be duplicated (e.g., 100, 102, 104, 106) to expand theimage so that the relationships may be defined in a non-overlapping wayfor complex definitions that define multiple relationships. The failureimage editor maintains the relationships within and between definedelements within the channels regardless of their vertical locationwithin the editor. Researchers then select a channel and the failureimage editor presents a set of events and binaries that are availablewhich apply to the given channel. Researchers may select any of theseelements and drop them on channel. Also, the researcher may create a newelement (event or binary) at any point within a channel (for exampleusing a right-click menu editor). Locations within the editor indicaterelative locations in time between selected and/or created elements. Ifa binary is dropped upon a location, the failure image editor determineswhether the beta or the alpha event belongs on the channel selected andplaces the event within the channel and the corresponding event (beta oralpha) on the channel indicated by the relational binary definition. Ifthe channel is not currently in the failure image editor then it isadded. Relational binaries that collapse down to a single icon (e.g.,cycling within a single channel) will show the single icon 110, 112rather than the alpha and binary events. The location of thecorresponding event is determined as the midpoint of the search windowdefinition. The entire window is shown as a set of parenthesis 116indicating the range of the search window relative to the correspondingevent, in this case a treatment event with an IV narcotic 114. Searchwindows are shown only within the beta channel of the relational binaryand the event itself is show within the midpoint of the search window.If an event is both a beta and an alpha event the search windowdisplayed is around the event is specific to the event when it isparticipating as a beta event. Search windows may be suppressed withinthe editor and/or shown only within the relational binary currentlyselected due to the fact that a single event may be the beta of anynumber of binaries. Individual events may be dropped onto a channel orcreated on a channel. New event types may be defined within the failureimage editor. Events may be connected with a drag-and-drop selection orwith an alpha and beta click selection, for example to define newrelational binary types.

The entire image or sections of the image may then be persisted as anaggregate failure mode. The failure image editor works in concert withthe aggregate failure mode editor to create and modify failure imagedefinition sets. Furthermore, the aggregate failure mode editor works inconcert with both the convergence editor and the event editor to createand modify the binary and event definition sets. In FIG. 20, thedefinition of aggregate failure modes is accomplished with asplit-screen view showing the failure image editor in the top pane 118while the aggregate failure mode editor is in the lower pane 120 showingan alternative type of failure mode diagram. These two models arecompletely synchronized with changes in one immediately reflecting thechange in the other.

In one embodiment researchers work from the bottom up to define failuresfrom a set of time series. Researchers may begin with a set of actualtime series from patients diagnosed with known failures, with a set oftime series generated by the processor to simulate certain conditions ora set of time series simulating no perturbation at all within a patient.This set of time series may be designated as immutable (for example withthe set of actual time series) or may be edited to provide a sample ofthe patterns being defined. Researchers may select portions of the timeseries which the failure image editor then will analyze to providecandidate event definitions. Alternatively the researcher may selectparameters to define an event and the time series displayed willindicate the results of that definition overlaid on top of the timeseries to provide visual guidance to the researcher. Once the researchercompletes the definition of an event the failure image editor willcompare that definition with other definitions within the same channel.If similar patterns are found the researcher is alerted and allowed tocreate a new event type or select one of the event types alreadyselected. If the event is a relational event, the researcher may selecta corresponding event from which relational parameters may be definedand experimented with or the researcher may simply define a function(e.g., >2×relative magnitude). Once an event has been fully defined thenthe researcher may choose to relate the event to another event withinthe image or to a search window within the image (e.g., to indicate amissing or null event). The researcher may indicate that aprocessor-ordered event as the beta of a relational binary. Groups ofevents and relational binaries may then be selected to define anaggregate failure mode.

In one embodiment, the failure image editor may be presented with alarge collection of time series sets provided with the indication of thepresence or absence of a particular known failure image. The failureimage editor creates a set of candidate definition sets refining them tocreate the right specificity and sensitively to match the sample set.Once the best-fit definition sets are created, a second large collectionof times series sets are provided with the indication of the presence orabsence of a particular know failure image. The failure image editorfirst uses the candidate definition set, determining sensitivity andspecificity, and then refines the definition set to be better suited ifpossible to both the first and the second collection of sample data.This process may be executed iteratively until a best-fit set ofdefinition sets is created or the process is deemed not to be asymptoticand is abandoned.

In one embodiment the failure image may be “played” or executed by theimage editor as a MPPC to provide further time-specific markers. Adefault execution of a failure image is “played” by placing all eventsas specified in their default (e.g., midpoint) location within theirrespective search windows as defined by the image definition. A sampleresult of this is displayed in FIG. 15A. Once the image is playedvertical markers are placed within the timeline as in FIG. 15A toindicate progressive states within an evolving image. In this way, theimage definition may be provided the specifications by which the imagestate may be identified and displayed within the patient safety monitor.

In an alternate and/or complimentary embodiment, the image editorprovides the ability to split the execution of an image into multipleintermediate and/or end states. Each different branch within the failureimage definition may be defined as a state within a failure image or adifferent, albeit related, failure image. Trees of related images may becomposed to provide alternative evolutions of failure within the failureimage definition.

FIG. 21 is a frame from a time lapsed motion failure image that includesa plurality of timelines from the patient illustrated in the failuremode diagram of FIG. 1. In this image, a patient who has experienced astroke has developed a condition associated with serum inappropriateantidiuretic hormone (SIADH), which induces a induced fall in serumsodium and confusion. The patient presented with an acute stroke but wasrecovering and alert. Then he slowly began to develop confusion and lessalertness. As the stroke was large, the nurses and physicians managingthe case thought that the patient's confusion and obtundation was due tobrain swelling. The patient SPO₂ and ventilation rate were normal, hehad no signs of sepsis and because of recently normal electrolytes, theattending physicians did not think that a metabolic cause for theconfusion was a reasonable option. In other words they misdiagnosed thepathophysiologic failure pathway (illustrated on the failure modediagram 200 of FIG. 1) and they thought the pathophysiologic pathway wasfollowing the direct connecting line 170 between stroke 208 andconfusion 220 as shown in the failure mode diagram 200 in FIG. 1.However, prior to the onset of the confusion the patient was receiving0.5 NS in spite of the fact that that he was eating and drinking. Repeatserum sodium confirmed a fall in sodium and SIADH was confirmed withadditional testing. Cautious correction of his sodium resulted in rapidrecovery and resolution of the confusion and obtundation.

Since the stroke caused the SIADH (which cased the fall in serumsodium), the actual modes of failure were significantly different thansuspected by the hospitalist in this case. Referring again to FIG. 1,the actual failure followed from the stroke 208 to the hyponatremia 242and then followed from the hyponatremia 242 to the confusion 220. Inthis case the patient survived the missed diagnosis but he experiencedseveral extra days unnecessary days in the hospital because of delay indetection and treatment of this failure.

FIG. 21 shows an image frame 2100 of a failure image editor forconstructing a range of MPPC for recognition by the processor 304 forthe patient described in FIG. 1. In this case the failure image shown isconsistent with presumptive severe sepsis. Theinflammatory/hemodynamic/respiratory augmentation 2110 is followed inthe image by a fall in VIO 2114 and metabolic failure with a rise inanion gap 2116. Note that if the inflammatory/hemodynamic/respiratoryaugmentation 2110 is unassociated with a rise in temperature 2118 (anull binary 2120 is identified), inflammatory mediator markers 2123 areordered to confirm the presence of the inflammatory component of thefailure image. The typical sequence of binaries is shown but theseevents may occur in any order. The processor 304 may provide greaterconfidence if the order is as shown and lesser confidence if the orderis different that shown. As noted failure images may overlap such thatpatient with preexisting hemodynamic instability may become septic, forthis reason, in this case the order is not deemed pivotal. However, forsome failure images the order of events may provide much greaterspecificity (in which case the parentheses may be adjusted accordingly.At first the failure image editor may be set to be more liberal and thenadjusted as hospital experience and quality improvement may dictate.

Now referring to FIG. 22, this exemplary image is derived from a patientwith the failure mode diagram of FIG. 1 having a timeline for a stroke,diabetes, atrial fibrillation, a history of congestive heart failure,and sleep apnea (in this case superimposed sepsis is not present). Thesecorrespond to the failure mode diagram of FIG. 1 illustrating potentialrelationships between stroke 208, diabetes 202, atrial fibrillation 206,congestive heart failure 204, and sleep apnea 210. Note that patientsafety processor is ordering routine confusion scores 185 because of thetimeline indicating a stroke. The detection of an increase in confusion185 or the presence of hypotonic saline administration 186 to a patientwith a stroke timeline automatically triggers a measure of electrolytes187 and upon the detection of a low serum sodium 188 the processororders a urine osmolarity 189 and indicates a high probability of SIADH190 and recommends an adjustment in fluid therapy 191. Here the problemis simple but the early signs of failure were at first subtle at a timewhen intervention would have prevented the increased length of staylater the pathways of failure were confused leading to further delay andconsiderable family since they were told the incorrect diagnosis. Inthis case the nurses and physicians may have been busy or may have beeninexperienced or simply not familiar with the subtle decline in mutationwhich may attend the development of SIADH in a stroke patient. Thereason subtle findings are missed is myriad. Note also, as illustratedin the failure mode diagram of FIG. 1, this is simply one failure andthere are very many potential failures for this complex patient.Furthermore, in this case the serum sodium was nearly normal when thelow sodium was finally detected so many physicians may not think thelevel was sufficiently low to cause these symptoms or warrantintervention. However, the sodium had dropped from a high normal to justbelow normal and in patient with brain edema the magnitude of the fallin serum sodium may be more significant than the absolute value and thisvariation in vulnerability from patient to patient and within the samepatient depending on coexisting disorders, diseases, and medications arenot concepts which are easily grasped by some healthcare workers whohave observed patients with very low sodium without any change inmentation. This illustrates the value of generating and recognizing amoving picture of the failure and care. The patient safety processordoes not trigger an alarm or define a diagnosis by a single thresholdbreach, because the system analyzes the entire failure and care imageover time and is programmed to recognize that this image indicatesvulnerability to a fall in serum sodium, even a fall that does not gobelow threshold. The patient safety processor provides the advantage ofcontinued vigilance and continuous consideration of all of the potentialphysiologic failures which are consistent with the images. According toone aspect, failure mode diagrams, such as the one in FIG. 1, may beused to construct prospective or retrospective failure images as in FIG.22 by applying the cascading binary relationships between diseases,treatments, and perturbations to construct failure images and imageranges using the failure mode editor.

The processor 304, as applied to the disclosed embodiments, is notconstrained by the exemplary definitions provided herein, but may rathercompare actual data to a plurality of MPPC images (stored or real-time)and image states to find best-fit matches. In one embodiment, thebest-fit matches may be determined by image registration techniques. Inembodiments, the matches may be made by image similarity measures thatinclude cross-correlation, mutual information, sum of squared intensitydifferences, and ratio image uniformity. The processor 304 may indicateall possible images and image states ranked by level of confidence. Forexample the processor 304 may indicate that a MPPC is consistent thesystemic inflammatory response syndrome with a high degree of confidenceand early septic shock with a medium degree of confidence and that TTP(and other potential alternatives) or overlapping failure modes areremotely possible in view of the image and remain to be excluded. Thephysician may be asked if it is desired to order the focused testing toexclude these remote alternatives or overlaps and/or the processor maybe programmed to automatically add this testing based on a specificrange of images (as defined, for example, using the drag-and-drop editordiscussed previously).

The identification of failure within the processor 304 is not the singleselection of a failure mode or a failure state, but the ranking of a setof images with regard to their fit within the data presented. Theidentification of multiple failure images is not simply the selection ofalternatives. Multiple failures may, in fact, exist and be interactingwith each other. Early states of some failure images may be verysimilar, or in fact exactly the same, as the early stages of otherfailure images or of a combination of failure images. The processor 304provides the analysis and visualizations that may allow the healthworker to understand the current state of the patient (and patientenvironment) in terms of possible future states—alternatives andcandidate overlaps—along with confidence levels as to theirspecification. Further, the processor 304 allows the health care workerto query the patient's condition with regard to confidence levels and,in particular, the comparative confidence level between two imagesand/or image states. For example, the confidence level for sepsis is lowwith the frame shown in FIG. 15B, whereas it is intermediate for fame inFIG. 15C and high for FIGS. 15D-15F. These confidence levels, along withthe action desired, may be programmed into the patient safety processorin advance by specialty groups, hospital safety committees, and/or maybe customized and “tuned” by individual physicians and or may be appliedadaptively by the processor by comparing the entered new diagnosis withthe present image and recoding that image as indication of that state.In the adaptive mode, the processor may be programmed to ask “is thisfailure image indicative of a failure process defined by this newlyentered diagnosis and, if so, please specify the first event, binary orimage component which in retrospect was part of this specific failureprocess”.

In one embodiment, the processor 304 may be trained by apathophysiological engine (such as a human simulator, as is known in theart) for the creation of failure and response images. Given a specifiedevent definition set and binary definition set, the patent safetyprocessor provides a dynamic image derived from the input of thepathophysiologic engine and the processor is instructed as to the natureof the images so that when these images are detected in the future theyare recognized. In one embodiment, a human simulator is connected to thepatient safety processor to provide an improved teaching tool forhealthcare workers. Researchers may select to be presented with anormal, unperturbed patient with various conditions. Once a dynamicimage of the patient is displayed researchers may introduce perturbationinto the pathophysiological engine which will result in new dynamicimages from the processor 304. For example, a research may selectrelationships presented according to a convergence and toggle them todivergence. Also, random divergence may be configured into the system.Divergence with respect to a single or a set of response system(s) maybe specified to model the breakdown of systemic response. Divergence maybe configure globally or for a specific timeframe indicating thatsystemic response fails, or is delayed. In this way, both perturbationand failure of systemic response may be selectively introduced to createfailure images. These failure images may be persisted to be furtheredited within the failure image editor. The researcher may selectseveral different variations and save them as failures and/or failurestates. These failures and/or failure states may be persisted within afailure component definition set to be used by the failure imageprocessor. Further, resultant failure images may be compared with actualpatient data to refine event and binary definition sets.

Alternately or in combination, according to one embodiment, an MPPC fromthe processor 304 may be simulated by a processor driving the humansimulator so that healthcare workers may observe the reanimation of theMPPC of the patient safety processor either as a digital animation or asa reanimation derived from output of a human manikin. One utilization ofthe embodiment that combines the pathophysiological engine to theprocessor 304 is to model treatment protocols. The engine may outputexpected or unexpected parameters (divergence) in response to treatmentand the image output of the patient safety processor may be observed,and/or recorded for protocol modeling. Further, using the ability tointroduce divergence, allows processed protocols or other protocols tobe verified for reasonable redundancy to cover failures of systemicresponse.

This aggregation of data, analysis and metadata provide the source ofdata for the patient safety visualization processor 372. In oneembodiment, the patient safety visualization process 372 provides avisualization of a patient's condition in a comprehensive groupingdefined by rows of timelines of specific signals and/or grouping and/orcategories of signals and/or signals. In one embodiment the global stateof each row is represented by color in a spectrum with a different colorfor each of: sustained stability, stability, convergence, perturbation,divergence, null, failure, cascading failure.

In another embodiment colored arrows, icons, text, and/or other visualrepresentations along each time line represent these states. In oneembodiment the patient safety visualization processor represents thepatient condition as a set of pixel streams moving from left to right toshow evolution of condition over time. The processor provides thenavigation backward and forward in time as well as up and down throughlevels of analysis within the patient safety image database 368. In anembodiment the levels of analysis may be, for example:

Time Series—Unanalyzed data streams in the form of time series

Events and Perturbation—Events and threshold violations characterizedwithin their respective channels as to whether they represent clearlydefined perturbation according to the event definition set 332

Systemic Response—Convergent, divergent and null binaries representingthe relationships between events, threshold violations, perturbationsand expected elements according to the binary definition set 344

Failure—aggregate failure objects representing images of failure thathave been identified within a single patient

System Failure—aggregate failure objects within a specific category(such as the respiratory system) representing images of failure thathave been identified within a single patient

Failure Patterns—Trends of failure and failure images within patientpopulation or a specific region, such as a specific hospital ward forexample.

In one embodiment the patient safety visualization processor 372composes an image on computer monitor (the patient safety console 384),which is composed by a series of pixels oriented horizontallyrepresenting data and analysis streams. These pixel streams are stackedvertically with the position on the x-axis representing a specific pointin time. The processor provides for the movement of the pixel streamshorizontally to provide a pan through time. Each pixel stream iscomposed of a set of pixels, which indicate the state of the data and/oranalysis at the specified point in time. The pixel has a state (e.g.,represented by color) and granularity (the length of time it represents[for example 1 minute]). The size of the view as well as the selectedspan of time determines the granularity of the pixel. In an embodiment,the pixel is displayed by the highest level of instability found withinthe time span represented by the single pixel within the pixel stream.

Further, each pixel has a level of abstraction, which determines whichobjects from the patient safety image database 368 contribute to itsstate. The contributing objects are shown below by level of analysis:

-   -   Time Series—data points within the channel (e.g., oxygen        saturation values)    -   Events and Perturbation—Events and threshold violations    -   Systemic Response—Relational Binaries    -   Failure—Aggregate Failure Objects    -   Failure Patterns—Failure Trends and Correlations

In an embodiment, groups of pixel streams are stacked vertically tocreate a patient safety visualization. Patient safety visualizations maybe composed of pixel streams of different patients or of data andanalysis streams within a single patient. Patient safety images providethe ability of the care worker to filter the analysis quickly toidentify problem areas or areas of a specific nature. Sorting may beprovided highlight emerging failure cascades or other pattern failures.

In an embodiment patient safety images may be composed of differentlevels of analysis displayed on the patient safety console 384 at thesame time correlated by time. The use of mixed-analysis levelvisualizations provides the careworker with the ability to quicklyunderstand the relationship between the lower levels of data (e.g.,incomplete recovery within oximetry) and the higher levels of analysis(e.g., the identification of narcotic-induced ventilation instability).

In an embodiment the patient safety console 384 provides the user theability to trace a failure condition back to the earliest eventsassociated with the failure to provide a visual display of a failurecascade. Alternatively, individual events and threshold violations maybe selected to identify which higher-level objects in which they playeda part. In other words, low-level events may be traced forward tounderstand their relationship within evolving patient instability. Thistracing, both backward and forward, is provided by the fact that alphaevents of a relational binary are often the beta event of a precedingrelational binary. This chain of relational binaries provides a powerfultool of analysis. The patient safety visualization processor providesthe ability to isolate these binary chains showing their origin,evolution and resolution. In one embodiment, visualizations may befiltered by the existence and character of binary chains.

In one embodiment, and if selected by configuration, the patient safetyvisualization processor provides the ability to navigate into themetadata models at any point within the visualization. Event,convergence and failure image component diagrams are accessible fromobjects, which were composed using specified elements within thesediagrams within the event definition set 332, binary definition set 344and failure image component definition set 356. Navigation into themetadata models provides expert care workers and researchers the abilityto further understand and/or alter the analysis.

The patient safety console 384 presents a complex set of data andanalysis that meets the immediate need of the busy care worker. In oneembodiment, analysis at the highest levels may be collapsed into asingle pixel stream or group of pixel streams per patient that providesa simple representation of the evolution of overall patient safety.Within and from that pixel stream the care worker may drill down intothe most complex displays: multiple levels of analysis, binary chainsand metadata models to name a few. Alternatively this drill down may beprovided by for example mouse over, touch screen, or may appearautomatically when the processor detects certain adverse patterns orthresholds.

In one embodiment, the object stream visualization focuses on therelationships and cascading of the onset of perturbation within thepatient. This is an alternate, and complimentary, view to the pixelstreams described above which focus to a greater extent on the state ofdiscrete elements within the system at various levels of analysis. Thesetwo visualizations may be used in parallel and/or provide navigationbetween them.

In an embodiment, the object stream visualization represents events andthreshold violations as icons along a time series in which the icon isplaced at the first point in time in which the event or thresholdviolation occurred. Icons indicate their character by color, size anddecorations. The basic icon is an arrow pointing either up or down (asin FIG. 15A). An up arrow indicates a positive movement, which triggeredan event whereas the down arrow indicates a negative movement. Booleanchanges will be indicated as an up arrow when moving from false to trueand a down arrow when moving from true to false. The thickness and/orcolor of the arrow may be used to indicate the extent of that movement.

Decorations on the arrow may be presented to provide visual cues as tothe nature of the event. A line underneath the head of the arrowindicates that he event that occurred was a threshold violation. Acircle around the arrow (see 979 of FIG. 15A) may be used to indicatethat the event was the result of a action or test ordered by the Patientsafety processor. Decorations and/or matching colors and/or flashingsmay be used to indicate a relationship warning by the processor, as inthe warning of the potential relationship between the low platelet countand the medication clopidogrel in FIG. 18.

In one embodiment, the patient safety visualization processor 372 willprovide automated visual navigation for a specified period of timeand/or specified images. This automated visual navigation acts as ananalysis-driven video playback of the selected period of time. Thehealthcare worker selects “Play” and allows the patient safetyvisualization processor to move visually through the evolution of aspecified condition. The healthcare worker may choose navigationmovements including “Play”, “Pause”, “Fast-Forward”, “Rewind”, “SkipForward”, “Skip Backward”, to name a few. In an embodiment, during Playmode the patient safety visualization processor moves at differentspeeds through the automated visualization depending on the severity ofthe condition being displayed. If the timeseries being displayed havelittle perturbation (or little perturbation related to the specifiedfailure cascade) the processor will move very quickly through time(i.e., from left to right). When an area of interest, as determined bythe processor, comes into vision the patient safety visualizationprocessor will slow the movement from left to right. Further, thepatient safety visualization processor will highlight elements thatindicate, clarify and specify the evolution and/or cascade of failure aswell as their relationships with other elements. The patient safetyvisualization processor will further display translucent pop-up panelsthat provide further textual and/or visualization elements to describethe current view and elements within the current view. At any point, thehealthcare worker may “Pause” the automated visual navigation to reviewthe displayed data and/or drill into what has been displayed.

In an embodiment, the healthcare worker may select from a summary view atimespan to review and also indicate sections of the timespan for whichthey are interested. The patient safety visualization processor willslow for the areas selected that are of interest and will increase thetextual and visualization display appropriately for the highlightedsections.

In one embodiment the patient safety visualization processor 304 selectsthe object streams to display and may include or remove streams as theybecome important in the video navigation. The healthcare worker maychoose to include additional streams or to “pin” streams so as to makethem always available in the video navigation. Missing streams are alsoindicated.

The patient safety visualization processor 372 may further indicate tothe healthcare worker the time estimated for automated visual navigation(e.g., “Standard visual navigation estimated at 2 minutes and 37seconds”). The patient safety visualization processor may include audioand visual elements corresponding to and synchronized with thetimeseries data along with timeseries data if video and audio feeds areavailable. In an embodiment, healthcare workers may include audio and/orvideo comments into the data streams to communicated and collaborateregarding elements displayed within the patient safety visualizationprocessor. The patient safety visualization processor may be directed toinclude all or a specified subset (e.g., “Include Comments from DoctorX”) of these elements within the automated visual navigation or may bedirected simply to indicate their presence such that the healthcareworker may invoke them as needed.

In an embodiment, the patient safety visualization processor 372 may“record” an automated visual navigation session into a non-interactivevideo format which may be viewed on standard video equipment, withstreaming technology or in a standard media player such that automatedvisual navigation sessions may be shared with healthcare workers who donot have access to the patient safety image database or the patientsafety visualization processor (e.g., as an attachment to an e-mail oraccessed from a video-enabled phone).

In one embodiment, the processor 304 may use an archive or database ofretrospective and/or theoretical model MPPCs as a source for determiningbest-fit image matches or as an ongoing model to improve such matches.As shown in FIG. 23, one embodiment is a patient safety processornetwork 2310 for archiving and cataloging a database of MPPCs and fordeveloping improved failure mode recognition, improved protocolization,and improved access of rural and underserved hospitals to timely failuremode detection and intervention. As shown, the network 2310 may alloweach hospital 2312, which are each in turn connected to respectivepatient safety processors 2314, to be connected to a central imagearchive, such as an MPPC archive 2316. The MPPCs from each patientsafety processor 2314 are uploaded to the central MPPC archive 2316 fromeach hospital. The central MPPC archive is connected to the databaseprocessor 2318, which serves to process the MPPC from the central MPPCarchive 2316 and to improve MPPC recognition and to develop new failuremode recognition and treatment protocols. MPPCs from a hospital patientsafety processor 2314 that are classified as associated with anobjectively known case, for example one that is confirmed independentlythrough additional tests (e.g., histopathology, genetic testing) orautopsy results, such as a MPPC suggestive of pulmonary embolismincluding a positive pulmonary angiogram, are input to the processor2318 to build an objectively defined MPPC database to further build thescope and specificity of the MPPC of pulmonary embolism. In thealternative, MPPCs that are classified as associated with an subjectivefinal diagnosis, such as an MPPC suggestive of SLE induced alveolarhemorrhage, for example followed by a opinion of a consensus group thatthis was the final diagnosis, may be added to the subjectively definedMPPC database case database to further build the scope and specificityof the MPPC of SLE induced alveolar hemorrhage. In this manner, a largedatabase may be derived from MPPC and image components of MPPC for theworldwide management of disease. International testing and treatmentprotocols based on the real-time MPPC detection may be developed thatmay potentially set a minimum standard of detection of catastrophicevents even in rural hospitals with a few beds, in urban hospitals whichare poorly staffed, and in environments wherein physician and nurseexperience may be very low. New protocols may be derived and uploaded tothese hospitals for their discretionary use as analysis of the MPPCresults in response to older protocols or new or additional treatmentoutside the protocols reveals potential for improvement. The approachhas the potential to provide improved surveillance of drug reactions andefficacy after, for example, the introduction of a new drug into aprotocol that may be an experimental protocol. Missing portions of theMPPC may also be identified to support the development of new testswhich fill in the gaps or perhaps reduce the number of tests ordered todefine cause(s) of the failure. Cost comparison of different testing andtreatment protocols may be performed.

The bandwidth of the MPPC may include tests, historic data, andtreatments that become objects in the MPPC. When potentially clinicallysignificant images of perturbation are identified in an MPPC, thepatient safety processor is programmed to quickly broaden the bandwidthto investigate the alternative causes. This is important because thelonger the duration an undetected failure mode the greater the increasein cost and mortality because complications develop with widen thecascade and make salvage more expensive and difficult. A narrowbandwidth (fewer tests) is, on the other hand (without considering thecost of allowing a longer duration of failure), less expensive than abroader bandwidth. The “effective bandwidth” includes those componentsof the bandwidth that actually contribute to characterize the factorsactively defining the failure image components of the MPPC. Poorlyconceived testing and treatment increases the bandwidth and the medicalcost but may not increase the effective bandwidth. One object of thepatient safety processors 2314 is to increase the effective bandwidth asrapidly as possible without broadening the bandwidth inordinately. In anembodiment, a patient safety processor medical system monitors with afew monitors and tests but uses these as sentinels, increasing thenumber of monitors and tests automatically if a MPPC begins.

Therefore, it may be advantageous to provide a mechanism toautomatically increase the effective bandwidth of the MPPC at any time(for example, during low staff times in a rural hospital), to optimallyshorten the duration of failure without the application of acontinuously wide and expensive bandwidth. One mechanism to broadenbandwidth is with improved testing, such as focused tests that have ahigh sensitivity and specificity for a specific failure mode. The MPPCarchive 2316 of the patient safety processor network 2300 may beexamined for opportunities to increase the motion picture bandwidth andachieving a balanced mechanism for mortality and cost reduction byshortening the duration of failure through earlier detection andimproved treatment response.

As discussed, according to one embodiment the patient safety processingnetwork includes a set of local patient safety processors located at ahospital ward or unit. The local patient safety processor is under thedirection of the healthcare workers at that location. This allows thelocal healthcare workers to control the treatment and testing protocols,and variation of the testing bandwidth, deployed for the patient undertheir control. The local attending physicians individually or as a groupas well as the hospital pharmacists and nurses may prescribe theseprotocols though the use of the Local patient safety processors. Thelocal patient safety processor records the healthcare worker(s) (forexample as a step time series of with an rise event occurring when thephysician, or nurse for example assumes responsibility and a fall eventwhen he or she is replaced by another. Those caring for the patient aretherefore part of the MPPC. Protocols may be decided by a group or by anindividual physician caring for the patient. The extent to which aparticular healthcare worker or group is statistically or otherwiseassociated with favorable or unfavorable MPPC may be assessed by theprocessor. The protocol choices for the local patient safety processorsmay be made through the use of pre prepared MPPC protocols as previouslydiscussed.

The local patient safety processor may recognize the physiciantime-series and adjust the protocols and MPPC to match those selected bythis physician. The physician may override the patient safety processorand if this occurs this override is an event rendering a new time seriesuntil the override is withdrawn. The extent to which a particularoverride is statistically or otherwise associated with favorable orunfavorable MPPC may be assessed by the hospital patient safetyprocessor, the hospital group patient safety processor, or the databaseprocessor 18. These may provide modifications in future protocols, andeven incorporation of the modification of the override or even theprevention of this type of override may be made accordingly.

The local patient safety processor s communicate with a hospital wide orhospital patient safety processor which is preferably under direction ofthe quality improvement committee and the hospital experts in eachfield. The hospital patient safety processor communicates with all thelocal patient safety processor s and may be used to upload treatment/andor testing and/or bandwidth adjustment protocols and or comparison MPPC,which have been agreed upon for application hospital-wide to the localpatient safety processors.

The hospital patient safety processor s of single hospitals communicatewith (and may be controlled by) central organization patient safetyprocessor. The organization patient safety processor allowsstandardization of the hospital protocols through the Hospital patientsafety processor s under its control to set a minimum safety treatmentand testing standards and may be controlled by a centralized qualityassurance group with expert representatives form all of the hospitals.Since the individuals caring for the patient represent at least one timeseries and the ward represents at least one time series and the hospitalrepresents at least one time series and the organization represents atleast one time series. The MMPP therefore includes all of theselocations. If the Patient is wearing a monitored GPS unit this maycomprise a location time series which provides continuous real timelocation as part of the MMPP. The patient safety processor s willcompare with the entered locations to identity convergence.

One embodiment demonstrates an example of how a new set of time seriesderived from testing devices and provided to the patient safetyprocessor may be evaluated for cost effectiveness. In this example, apulse oximetry reflectance probe is mounted (as by hat or headband orother fixation device above at least eye to the patient's head and theprobe is wirelessly or otherwise connected to pulse oximeter and thelocal patient safety processor (as by Bluetooth for example). Thetransmitter may be mounted in the probe, on the headband or hat orbehind the ear in the position of a hearing aid if desired. A positionsensor may also be provided mounted on the patient. A maneuver such as achange in body position may be detected and included as an event by thepatient safety processor and a fall a component of thephotoplethysmographic pulse (indicative of the perfusion of thecapillary bed distribution of the supra-orbital artery, a distal branchof the internal carotid) in relation to a maneuver. In this way the flowof the capillary bed above the eye becomes a surrogate marker of othercapillary beds supplied from the internal carotids. Real time perfusionmay be compared with that of the ear, fingertip, or the pulse pressure(as by an invasive arterial line for example) to identify disparate inperfusion in one or both of the internal carotid distribution. The localpatient safety processor processes the MPPC with these as additionaltime-series. The local patient safety processor uploads the MPPC to theHospital patient safety processor, and the hospital patient safetyprocessor, organizational patient safety processor, and/or databaseprocessor 18 where the MPPCs may be evaluated to determine if afteradjusting for disparities in the MPPCs as a function of co-morbidities.The MPPCs that include the time series derived from the supra-orbitalplethysmographic pulse may be associated with a reduction in the numberof falls in the hospital. If this is statistically significant, thesetime series may be automatically added (by automatically ordering theintermittent or continuous supra-orbital monitoring used the study) toincrease the testing and bandwidth when it is detected that the MPPC ofa given patient is similar to those of the study population where theaddition of those processed time-series data had a positive impact onoutcome.

The database processor 18 is preferably connected to all theorganization patient safety processor s (or hospital patient safetyprocessor s if the hospital is not under a central organization). Thedatabase processor 18 is preferably controlled by a healthcareinformation corporation which maintains the database processor 18 andthe network. Each patient safety processor below the database processor18 is capable of operating independent of the patient safety processornetwork so extensive redundancy, lack of subordinate dependency, andtherefore greater safety against network failure is built into thepatient safety processor network.

This patient safety processor network structure allows diverse minimumstandards to be set by each government and allows the monitoring of theeffects of these diverse minimum standards to determine cost andbenefit. The database processor 18 preferably includes a comparisonprocessor that compares the MPPC and all of the objects of the MPPC,such as events, binaries, image components, and cascades, to other MPPCsall of the objects of the other MPPCs to identify statically differencesbetween the MPPC which are associated with improved or adverse cost,outcome, length of stay, morbidity, mortality, resource consumption,and/or complications. One advantage of the patient safety processor isthat the objects of the MPPC are discrete and are therefore readilyincorporated into statistical software components of the PSCP. Thestatistical software components may include a wide array of statisticalsoftware products as are well known in the art for identifyingdifferences in discrete time related data collections. The objects alsocomprise organized collections of an ascending hierarchy of complexityand the organized collections which may be compared statistically ateach ascending level of complexity to identify associated differences.In one embodiment the PSCP divides the MPPCs into groups having a leastapportion of substantially the same image components. For example agrouping may be derived having substantially the same initial sepsiscascade picture and similar co morbidities and age and sex but differentphysicians, hospitals and/or treatments. Differences in length,progression, compilations and mortality associated with the cascade maybe identified and statistically compared with the differences inphysicians, hospitals, treatments, testing, and/or treatment timing.

When a particular testing, treatment, bandwidth variation, wardlocation, or hospital location is identified as statistically associatedwith improved outcome then the database processor 18 my offer, as fordownload, new protocols which incorporate those identified particularsinto the hospital patient safety processors and/or Organization patientsafety processors for their consideration. New medication or treatmentsmay be assessed in this way with blinding of the data accommodated bythe patient safety processor such that the time series of theexperimental medical is labeled with an experimental code.

While the disclosed embodiments may be susceptible to variousmodifications and alternative forms, specific embodiments have beenshown by way of example in the drawings and have been described indetail herein. However, it should be understood that the disclosure isnot intended to be limited to the particular forms disclosed. Indeed,disclosed embodiments may not only be applied to clinical diagnosis ofsystems of physiological failure, but may be applied to any clinicalcondition that may be represented by images as provided herein. Indeed,the disclosed embodiments may be applied to monitor and/or diagnoseconditions in which a patient's condition is generally improving, suchas post-surgical monitoring. Rather, the disclosure is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the disclosed embodiment and as defined by the followingappended claims.

1. A hospital monitoring system comprising: a database comprising anelectronic medical record repository of data wherein the electronicmedical record repository of data contains the data of a plurality ofpatients in at least one hospital; and a processor programmed withinstructions for: searching the database for at least one complexcascade pattern within the data of the plurality of patients and toidentify one or more patients generating the at least one complexcascade pattern.
 2. The hospital monitoring system of claim 1,comprising an alarm processor programmed with instructions to provide anautomatic alarm upon the detection of the complex cascade pattern. 3.The hospital monitoring system of claim 1, wherein the complex cascadepattern comprises a cascade of at least one of sepsis, severe sepsis,septic shock, microcirculatory failure, and shock.
 4. The hospitalmonitoring system of claim 1, wherein the complex cascade patterncomprises at least a plurality of linked perturbations and/or trends ofphysiologic and laboratory data creating a progressively enlargingaggregation of progressively greater numbers of perturbed physiologicand laboratory data.
 5. The hospital monitoring system of claim 1,further programmed to determine at least one characteristic of thecomplex cascade pattern, the characteristic comprising at least one of aseverity of the complex cascade pattern, a duration of the complexcascade pattern, a time of onset of the complex cascade pattern, amaturity of the complex cascade pattern, a timing relationship of thecomplex cascade pattern to other events or another complex cascadepattern, a cost associated with the complex cascade pattern, a globalpattern of the complex cascade pattern, a time of termination of thecomplex cascade pattern, components of the complex cascade pattern, astate of evolution of the complex cascade pattern, a length of staysubsequent to, or in association with the complex cascade pattern, ortreatments associated with the complex cascade pattern.
 6. The hospitalmonitoring system of claim 5, wherein the at least one characteristic ofthe complex cascade pattern is defined by at least one of a number ofperturbations and/or trends which comprise the complex cascade pattern,a severity of the perturbations and/or trends, a number of systemsaffected by the complex cascade pattern, a presence, number and/orseverity of failure of compensation in response to perturbationsassociated with the complex cascade pattern.
 7. The hospital monitoringsystem of claim 1, wherein the processor comprises instructions fordetermining a rate of growth of the complex cascade pattern.
 8. Thehospital monitoring system of claim 1, wherein the processor comprisesinstructions for determining the rate of growth of the complex cascadepattern by at least one of the increase in number and/or severity of newperturbations being added per unit time, the increase number of systemsaffected, and the increase number of perturbations present in differentsystems.
 9. The hospital monitoring system of claim 1, wherein theprocessor comprises instructions for detecting events or components thatare temporally and/or spatially associated with the complex cascadepattern but that are not part of the complex cascade pattern.
 10. Thehospital monitoring system of claim 1, wherein the processor comprisesinstructions for converting the electronic medical records into a formatfavorable for searching for the complex cascade pattern.
 11. Thehospital monitoring system of claim 10, wherein the format comprisessequential and timed variations comprised of at least positivevariations and negative variations of the data.
 12. The hospitalmonitoring system of claim 10, wherein the electronic medical recordrepository of data comprises data from a plurality hospitals, andwherein the processor is programmed comprises instructions foridentifying the patients that are generating complex cascade patternsand the hospital in which they are located.
 13. A patient dataprocessing system comprising a processor programmed to: convert theelectronic medical records of at least one hospital into sequential andtimed trends comprised of at least positive trends and negative trendsof both the physiologic parameters and the laboratory data, detectrelational trends comprised of a combination of positive and/or negativetrends, detect complex cascade patterns comprised of a plurality ofcombinations of relational trends, automatically output a display of theimage of the detected complex cascade, automatically output a warningindicating the detection of the complex cascade and the identificationof the patient generating the complex cascade, track the growth ordecline of the complex cascade and output an indication indicative ofgrowth or decline.
 14. The patient data processing system of claim 13,wherein the complex cascade pattern is indicative of physiologicfailure.
 15. The patient data processing system of claim 13, wherein thephysiologic failure is at least one of sepsis, severe sepsis, septicshock, and microcirculatory failure, a shock cascade, and a septic shockcascade.
 16. The patient data processing system of claim 13, wherein theprocessor comprises instructions for to determining and outputting anindication of the type of the cascade detected.
 17. The patient dataprocessing system of claim 13, wherein the processor comprisesinstructions for to determining and outputting at least an indication ofthe timing and type of the trends along the cascade.
 18. The patientdata processing system of claim 13, wherein the processor comprisesinstructions for to determining and outputting at least an indication ofthe length of the cascade.
 19. The patient data processing system ofclaim 13, wherein the processor comprises instructions for to detectingthe onset of therapy determining and outputting at least an indicationof timing of therapy in relation to the cascade.
 20. A patient dataprocessing system for processing electronic medical records of ahospital, comprising a processor programmed to: generate a time-seriesof data of at least a portion of a plurality of patients in thehospital, including at least data relating to the physiologic stateand/or care of each patient; convert the datasets, including at leastthe monitored datasets and laboratory datasets into parallel andoverlapping time series; identify occurrences comprising inflammatoryoccurrences, metabolic occurrences, volumetric occurrences, hemodynamicoccurrences, therapy occurrences, hematologic occurrences, orrespiratory occurrences; identify the timing of the occurrences;identify at least one relational pattern of occurrences along aplurality of time series that is indicative of failure cascade of atleast one of a sepsis cascade, a pulmonary embolism cascade, a metaboliccascade, or a microcirculatory failure cascade; and output an alarm whenthe failure cascade is detected.